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Full text of "The Effects of governance corruption on education budgets and income in central and eastern Europe"

THE EFFECTS OF GOVERNANCE CORRUPTION ON 

EDUCATION BUDGETS AND INCOME IN 

CENTRAL AND EASTERN EUROPE 

by 

Tamara Lynn Hannaway 

BA, Fort Lewis College, 1984 

MBA, Westminster College, 1995 



A thesis submitted to the 

Faculty of the Graduate School of the 

University of Colorado Denver in partial fulfillment 

of the requirements for the degree of 

Doctor of Philosophy 

Public Affairs 

2012 



© 2012 by Tamara Lynn Hannaway 



All rights reserved. 



This thesis for the Doctor of Philosophy degree by 

Tamara Lynn Hannaway 

has been approved for the 

Graduate School of Public Affairs 

by 



Peter deLeon, Chair 

Paul Teske 

Robert Reichardt 

Christoph Stefes 



Date 



in 



Hannaway, Tamara Lynn (Ph.D., Public Affairs) 

The Effects of Governance Corruptions on Education Budgets and Income in 

Central and Eastern Europe 

Thesis directed by Professor Peter deLeon 

ABSTRACT 

This thesis addresses economic development in the context of endogenous corruption. 
We also ask whether economic growth exacerbates poverty or income inequality. The evidence 
to date is mixed. The thesis examines relationships among and between defined constraints on 
economic development by offering policy makers a unique method of measuring governance cor- 
ruption's effects on education budgets and individual income. Governance corruption includes 
malfeasance, misfeasance, nonfeasance, or perpetrations involving state, non-state, and private 
sector actors that circumvent, distort, or manipulate the democratic process, and thereby under- 
mine the government's revenue stream. Governance is the official governmental system and 
institutional 'rules of the game' by which a country is governed; state capture, rent seeking, and 
free riding behaviors corrupt the system. The Shadow Economy, acting as the surrogate for cor- 
ruption, measures the percent of total productivity unaccounted for in the official GDP. The 
individual actor is the unit of measure; Central and Eastern European countries are the sample set; 
individual income is the dependant variable; and the independent variables are the Human Devel- 
opment Index, education expenditures, and the Shadow Economy. The analyses presented 
suggest clear evidence that as the size of the Shadow Economy increases, the budget for educa- 
tion expenditures as a percentage of the total national government expenses decreases. The 
evidence implies that as the education budget decreases, so does the official individual income, 
and therefore, available measures for economic growth are inadequate to measure income ine- 
quality, thereby leaving analyses and conclusions regarding the effects of economic growth on the 
individual actor, wanting. These findings are consistent with New Growth Theory, particularly, 
that education is critical to a healthy and sustainable economic development, and offer evidence 
that adding the effects of corruption to current economic growth models provides unique learning 
about growth's effects on income inequality. The practical application is that education expendi- 
tures and individual income are analyzed together and in light of the effect of corruption on them. 
This evidence may be appreciable to economic development and education policy making. 

Key words: Governance, Corruption, Education Expenditures, Income Inequality, New Growth 
Theory, Shadow Economy, Human Development, Economic Growth, Economic Development, 
Sustainable Development. 

The form and content of this abstract are approved. I recommend its publication. 

Approved: Peter deLeon 



IV 



DEDICATION 

I dedicate this work to my daughter Hannah, with a very special thank you for your love, 
smile, vibrance, and the joy you bring. For your love, patience, inspiration, and support, thank 
you, especially to my mom, dad, grandma, sister, and brother, and also to my extended family, 
friends, colleagues, mentors, and students. 

Many persons from childhood to today deserve mention and a debt of gratitude. They 
have imparted wisdom, contributed some important element, provided inspiration, influence, and 
encouragement. The list is many years in the making, and longer than this dissertation; thank 
you. 



ACKNOWLEDGEMENTS 

Many thanks to my advisor, Dr. Peter deLeon, for believing in me from the start, for his 
guidance, and for his contribution to and support of my research. I also wish to thank all the 
members of my committee for their valuable participation and insights. 

From before the start of this Ph.D. process, Colorado Christian University has been my 
place of full-time employment. The extraordinary grace, understanding, and support extended to 
me by the whole of the institution and its employees, my colleagues, the administration, and the 
staff, is greatly appreciated. Thank you. A more ardent team of cheerleaders the academy has 
never known. 



VI 



TABLE OF CONTENTS 

CHAPTER 

I. INTRODUCTION 1 

Research Questions 2 

Problem and Research Approach 9 

Sample Set Rules 16 

Thesis Overview 17 

II. LITERATURE REVIEW 18 

Governance 19 

Measuring Governance 22 

Good Governance and Sustainable Development 24 

Human Development as a Metric for Governance 28 

Corruption as a Dimension of Governance 31 

Forms, Size, and Scope of Corruption 33 

Shadow Economy Rules 37 

Causes and Consequences of Corruption 37 

Corruption's Remuneration 40 

Controlling Corruption 41 

Consequences of Corruption on Development 43 

Measuring Corruption - Indirectly 44 

Economic Growth 49 

The Business Cycle - the foundation of economic growth stages 49 

Economic Growth Theories 52 

Measuring Education Delivery 64 

Measuring the Quantity of Education (Supply) 64 

vii 



Measuring the Value of Education (demand) 64 

Governance Corruption in Education 65 

Summary 71 

III. THE DATA AND METHODOLOGY 72 

Methodology 72 

Hypothesis 73 

Data 73 

IV. ANALYSIS 83 

Research Question 1 83 

Research Question 2 86 

Research Question 3 91 

Research Question 4 94 

Hypothesis 4.1 94 

Hypothesis 4.2 96 

Hypothesis 4.3 98 

V. FINDINGS 101 

Summary 109 

Data Limitations 117 

VI. CONSLUSIONS AND FUTURE RESEARCH 122 

Conclusions 122 

Future Research 128 

APPENDIX A: COUNTRY BRIEFS 133 

APPENDIX B: DATA RELIABILITY AND VALIDITY 187 

TABLES AND FIGURES 227 

GLOSSARY 243 

REFERENCES 246 

viii 



LIST OF FIGURES 

Figure 1.1 Change in Income per Capita and Change in Human Development Index 84 

Figure 1.2 Change in Income per Capita and Change in LEI and EAI 85 

Figure 3.1 Shadow Economy's Effects on Future Education Expenditures 93 

Figure 3.2 Shadow Economy's Effects on Current Education Expenditures 93 

Figure 5 Shadow Economy MIMIC Diagram 75, 232 

Figure 5 . 1 Change in Income per Capita and the Education Expenditure 1 05 

Figure 5.2 Change in Income per Capita and Lagged Education Expenditure 105 

Figure 6 Shadow Economy Simultaneous Equations 232 

Figure 6.2 The Kuznets Curve (1966) 129 

Figure 6.3 Educational Kuznets Curve (author's depiction) 130 

Figure 7 Data Validation Comparison 239 

Figure 8 The Policy Problem 246 



IX 



LIST OF TABLES 

Table 1 Hypothesis 1 Data 228 

Table 2 Hypothesis 2 Data 229 

Table 3 Hypothesis 3 Data 230 

Table 4.0 Equation 4 Comparison 107 

Table 4.1 Correlation Coefficient Matrix 242 

Table 4.2 Correlation Coefficient Matrix 242 

Table 4.3 Correlation Coefficient Matrix 242 

Table 4.4 Correlation Coefficient Matrix 242 

Table 5 GDP per Capita Cycle 233 

Table 7.1 Data Validation 234 

Table 7.2 Data Validation Equation Analysis 240 

Table 7.3 Data Sources 241 

Table 7.4 Correlation Coefficient Matrix 192, 242 



PREFACE 

This thesis analyzes public policy through an economic development policy lens and 
framework. The purpose of this thesis is to inform economic development policy through the ex- 
amination of relationships among and between defined influences and constraints on economic 
development, and to offer policy makers a unique method of measuring governance corruption's 
effects on education budgets and individual income. The approach used is to compare and con- 
trast 1990 and 2008 economic development as measured by Gross Domestic Product per Capita, 
or Individual Income; the Human Development Index and its component indices; governance cor- 
ruption as measured by the "Shadow Economy" (Schneider et al., 2010, p. 5); and Education 
Expenditures as measured by United Nations Educational, Scientific and Cultural Organization 
(UNESCO). 

The research herein covers broad areas of literature from the social sciences; political sci- 
ence, history, corruption, governance, and economics; and from business, accounting, and 
education. The unit of measure is the individual. Actors in this thesis may be employees of the 
state, of non-state institutions, or work in the private sector. Official income per capita is stated 
in US dollars using the year 2000 as a base year. Narrowing the scope of this array of literature 
was based on an experience. 

While standing in the rubble of what recently was the Berlin Wall in 1989, 1 looked east, 
then west, the east again. Grey, Color, Grey. Stooped, vibrant, stooped. Battered, flourishing, 
battered. A woman, standing behind a smallish grungy table, was selling bits of the wall, 
stamped with what she stated was some official seal. I bought one, just in case there was such a 
seal, and picked up another from the piles upon which the tourists walked and children played. 
The image of dichotomy, contrast, and dissimilarity, in my visual perspective overwhelmed my 

senses. I knew a few facts about me \„uiu war, uut the textbooks said nothing of what 

my eyes could see. I felt a communal sense of anguish flowing from the east, while the west was 

xi 



as familiar as my own United States. Some entity or organism, some insidious, living thing lying 
to the east, beyond the government or its structures and peace accords, beyond the empty promis- 
es believed by the proletariat, was causing the pervasive agony. 

Repeated questions tumbled about in my head for a score of years, through several visits 
to the same and other places behind what was the Iron Curtain. 1) What festering plaque prohib- 
iting citizens from living a life fulfilled? 2) Why could the people not shake it off, loose it, 
overcome it, beat it? 3) Who were the guardians of the people; where were these sentries; and 
why did they not act on behalf of the millions of downtrodden? 4) How did the physical infra- 
structure decay and the economic powerhouse implode? Thus, the variables for this thesis 
became: 1) Corruption. 2) Education. 3) Governance. 4) Economic policy. 



xn 



CHAPTER I 
INTRODUCTION 

This thesis endeavors to inform economic development policy so as to encourage healthy 
economic growth (Kuznets, 1966, p. 493) without the friction of institutional or political corrup- 
tion. We also ask whether economic growth exacerbates poverty. The evidence to date is mixed. 
Deceiving in its simplicity, this fundamental question is of paramount importance as factors of 
globalization induce growth in the world's Gross Domestic Product (Levitt, 1983). To inform 
development policy toward a more balanced growth, this thesis examines relationships among 
and between defined influences and constraints on economic development, and specifically, it of- 
fers policy makers a unique method of measuring governance corruption's effects on education 
budgets and individual income. "Growth," neither culprit nor remedy, is the benign measure of 
influences on economic productivity over time (Kuznets, 1973). The terms for economic change, 
growth and development in this thesis, follow the definitions advanced by Schumpeter (1939) and 
Kuznets (1934, 1940). Economic growth is incremental change, generally measured by change in 
Gross Domestic Product (GDP). Economic development is a new steady state. This new level of 
development is realized in response to economic growth and the evolution, health, maturation, 
and increased capacity of the economy to sustain growth. 

This thesis advances and challenges New Growth Theory (Romer, 1990) by investigating 
how specific measures of governance, and the corruption within governance, affect growth of in- 
dividual income; further, it explores linkages between these factors and the causal relationships 
among them (Barro, 2001b; Sen, 1997, 1999). Data from nations under the former Soviet spheres 
of influence during the Cold War have extraordinary potential to shed light on governance and 
development policy. While international attention focused on the transitions from Soviet rule to 

independence, intergovernmental organizations and academics seized the opportunity for re- 

1 



search, documentation, and data collection. Meanwhile, advances in technology and computing 
power grew exponentially. The breadth and depth of data available for analysis on the conse- 
quences of growth are unprecedented. 

Research Questions 
Research Question 1: Are the Human Development Index and Income per Capita highly corre- 
lated in the sample set? 
Research Question 2: Does governance corruption, as measured by the Shadow Economy, nega- 
tively affect Income per Capita? 
Research Question 3: Does governance corruption, as measured by the Shadow Economy, nega- 
tively affect Education Expenditure? 
Research Question 4: Do the pre-test Human Development Index, governance corruption as 
measured by the Shadow Economy and Education Expenditure together explain the 
change in Income per Capita? 

The key hypothesis this thesis tests is: governance corruption's effects on education 
through the public resource mechanism, its budget, are direct and negative; succinctly, the higher 
the degree of corruption, the lower the relative education budget. Further, the lower the education 
budget per capita, the lower the relative individual income. 

The evidence varies on whether economic growth exacerbates or alleviates the relative or 
absolute income at the national level. Evidence at the level of the individual actor is far more ob- 
scure (Galbraith & Kum, 2005). For this reason, the focus of this thesis is the total income to the 
individual actor. 

A comparison of the multinational evidence indicates the presence of four conceptual 
challenges: First, how do we define and measure individual income? Second, when or what pe- 
riod should we measure? Third, what in addition to income provides a more thorough picture of 
the living standard of the individual? Lastly, which aspects of governance corruption effect indi- 



vidual income? These are important questions for policy makers, as national (aggregated) figures 
mask the effect of policy decisions at the individual level. For example, measured from 1990 to 
2007, Uzbekistan's GDP increased 1.2 billion (US equivalent dollars) or 1.8 percent, while the 
individual income decreased from $3,155 to $2,425, or 30 percent, over the same period. 

The first conceptual challenge stems from inconsistent definitions and measurement 
methods, which seem to report contradictory evidence. For example, Lozada describes one side 
of the debate: "The fierce debates (among). ..[a]cademics, journalists, and multilateral organiza- 
tions. ..over economic globalization have focused recently on global poverty and income 
inequality.. .and a general consensus seems to have formed around the proposition that poverty 
and inequality are on the rise" (2002, p. 5). Maddison's data show that since 1820, the average 
yearly world GDP growth is 2.21 percent, while GDP per capita growth is 1.2 percent (2009, p. 
4), suggesting that the consensus should be that aggregate and individual incomes levels have di- 
verged. On the contrary, Sala-i-Martin's data, measured in both absolute and relative terms, show 
that, while the world population has steadily increased, fewer people live in poverty today than at 
any time in recorded history, and empirical evidence shows converging income levels and an 
emerging "world middle class" (2002, p. 2). Making sense out of what seem to be contradictory 
findings would require an exhaustive analysis of the underlying data sets, a justification for and a 
comparison of the definitions and measurements of the variables, (Pritchett, 1997, pp. 12-13), ef- 
forts beyond the scope of this thesis. 

Instead, this thesis employs demographic and economic data produced and shared 
through data networks, and the International Comparisons Program (ICP). Scholars, academics, 
and professional researchers affiliated with lists of international agencies {e.g., United Nations, 
World Bank, International Monetary Fund), intergovernmental organizations (IGO), and non- 
governmental organizations (NGO), including abbreviations as used in this thesis, share data (See 
detailed list of affiliated organizations in the Glossary). This network of agencies provide data 



for public use, which usually include detailed methodology, reliability, and validity statistics 
(HDR, 2007). Institutes such as Brooking Institution (BI), Transparency International (TI), The 
Heritage Foundation (HF), International Comparisons Program (ICP), European Statistics, Data, 
and Metadata Exchange (SDMD) (here after, Institutes), feed critical research and data to the 
network. The first conceptual challenge is thus met by using data from the same network of 
sources. 

The second conceptual challenge stems from inconsistent measurement methods that led 
to comparing dissimilar periods. Barro (1991) and Solow (1956), among others, claim that in- 
comes converge over time. Generally, scholars who report that incomes converge favor 
analyzing the longest time span with the most or best available cross-country data (Barro & Sala- 
i-Martin, 1991). Conversely, other scholars, who favor analyzing specific periods, argue that the 
development situation or stage of each country's economic growth matters when measuring ine- 
quality (Rostow, 1991). The latter group offer evidence that incomes diverge when tied to certain 
circumstances in history. This point, data knitted with situations, is central to the purpose of this 
thesis and solving its questions (Matheson, 2008; Pritchett, 1997; Rostow, 1991). Measuring an 
economy from one arbitrary date to another based on data availability may invite risk. The risk is 
missing vital information about the characteristics of economic growth specific to each country, 
its quality and sustainability, and the path, patterns, or cycles of the growth, some of which is 
available through historical accounts. 

One of the first scholars to trace the paths of income over time was Simon Kuznets. 
Kuznets (1966) invested much of his extraordinary career examining questions about income dis- 
tribution, economic measurement methods, and growth. He advanced theories that numerous 
scholars, including several fellow Nobel Laureates (e.g., Robert Solow, Douglass North, 
Amartya Sen, Gordon Tullock, Edmund Phelps, Paul Krugman, Herbert Simon, Milton Fried- 
man, and others, plus several whose work is not central to this thesis), have studied, tested, 



refuted, or confirmed on how and why economies grow. Kuznets argued that the trajectory of in- 
come growth depended on the stage of development in a country. He found that the paths of 
higher and lower incomes in lesser-developed, more agrarian societies tend to diverge during pe- 
riods of economic growth, thus increasing income inequality, while incomes in more developed 
societies tend to converge as wealth distributes over a greater percentage of the population. The 
second conceptual challenge is met by anchoring the data to a regime change (Rostow, 1991). 
Following work by Matheson (2008), Pritchett (1997), and Xu & Li (2008), accounting for the 
stage of development (adding the stage as a variable to a development function), creates a frame- 
work that invites conditioning variables (e.g., regime, governance, institution), and makes sense 
out of the standard economic variables (e.g., GDP, life expectancy, educational attainment, trade 
alliances) by anchoring them to a common phase or event (e.g., industrial revolution, inventions, 
growth stages, policy stages, regime changes, intrastate armed conflict, treaties, the end of the 
Soviet Empire) (Brewer & deLeon, 1983; Rostow, 1991; Xu & Li, 2008). 

The third conceptual challenge rests in the defining of living standards by a simple dollar 
figure. While the convergence/divergence debate just described persists, Sen (1984) questions its 
relevance. He asserts that examining the income level between the richest and poorest in a socie- 
ty may be in vain, as income per se may not reflect the reality of human development, yet GDP 
per Capita (Income per Capita or Ic) is often used to infer or approximate living standards in the 
literature (Deininger & Squire, 1996; Gini, 1921; Kuznets, 1934; Sen, 1984). The third concep- 
tual challenge in this thesis is that empirical evidence on income changes over time neglects 
evidence of diverging living standards - which has been the origin of media coverage, the Mil- 
lennial Development Goals, and even armed conflicts. In the Forward to the 2008 Millennial 
Development Goals Report, Sha Zukang wrote, today "...2.5 billion people, almost half the devel- 
oping world's population, live without improved sanitation; [m]ore than one third of the growing 
urban population in developing countries live in slum conditions" (2008d, p. 4). The third con- 



ceptual challenge is met by employing the Human Development Index (HDI) as a measure for the 
living standard. The work of the Human Development Program, in its 20 th year at the United Na- 
tions, has provided a center for researching and measuring the living standard (HDR, 2009e). 

The last conceptual challenge is in defining governance corruption, and for the purposes 
of this thesis, limiting its scope to that which effects economic growth policy, and limiting its 
pervasiveness to that which policy may be able or interested to ameliorate. The objective is to 
isolate "corruption [that] alters the composition of government expenditure" (Mauro et al., 2002, 
p. 277). Bracketing the span of the state (official), non-state (institutions), and private sectors to 
isolate expenditure-altering types of corruption are the bodies of literature on (1) political corrup- 
tion and the (2) unofficial economy. Political corruption is, "a co-operative form of 
unsanctioned, usually condemned, policy influence for some type of significant personal gain, in 
which the currency could be economic, political, or ideological remuneration" (deLeon, 1993, p. 
25). Unofficial influences include ". . .those economic activities and the income derived from 
them that circumvent or otherwise avoid government regulation, taxation or observation," which 
are measured by the Shadow Economy (Schneider et al., 2010, p. 1). This thesis employs the fol- 
lowing working definition. 

Governance corruption is a co-operative form of unsanctioned, usually condemned, poli- 
cy influence that circumvents or otherwise avoids government regulation, taxation, or 
observation, and alters the composition of government expenditure for some type of significant 
personal gain, in which the currency could be economic, political, or ideological remuneration. 

Can multiple scholars with conflicting theories and evidence on economic development 
and income levels be right simultaneously? Perhaps. A summary of the conceptual challenges 
facing researchers and solutions for this thesis follow. 

The first challenge is simultaneous convergence and/or divergence in income and/or liv- 
ing standards, which suggest dichotomous definitions and/or uses for these terms and permits 



dissimilar data and/or measurement. No wonder scholars disagree. Remedy: Utilize International 
Comparisons Program data. 

The second challenge is simultaneous converging and diverging results, using the same 
data and methods, which suggests a problem of arbitrary data periods or date ranges unidentified 
with and/or tied to events, endogenous or exogenous. Solution: Tie data to the fall of the Berlin 
Wall, the end of the Cold War, and analyze the 30 countries of the former Eastern Bloc (See 
Country Briefs). 

The third challenge: Income is an insufficient measure of the human condition. Remedy: 
The Human Development Index as a proxy for the living standard. 

The fourth challenge is to narrow the scope of corruption to that which (1) is found in the 
governance process and (2) is measurable and missing from the official GDP. Solution: Employ 
the working definition of Governance Corruption, measured by the Shadow Economy as a proxy 
for the missing GDP. Adding background information, or context, makes these challenges easier 
to understand. 

The historical backdrop for this thesis's economic development process follows. Histori- 
cally, like today, international trade and the migration of people and resources have driven 
national economies; shifting prosperity and poverty, technology adoption, and the intermixing of 
cultures (Diamond, 1997; Elisseeff, 1998). Aided by advances in technology and modes of 
communication, escalating globalization has fueled a blur of activity resulting in increased inter- 
dependence of nations (T. L. Friedman, 2005). Why have some nations struggled while others 
flourish? Levitt (1983) argued that "A powerful force drives the world toward a converging 
commonality and that force is technology... Two vectors shape the world - technology and glob- 
alization. The first helps determine human preferences; the second, economic realities" (p. 1). 
Nye (2006) adds that, "Globalization has two driving forces: technology and policy. Thus far, 
policy has reinforced the. . . effects of technology" (p. 1). Specifically, technology renders dis- 

7 



tance (from one village or hemisphere to another) progressively less important. Accordingly, 
economic development or decay necessarily takes place in the larger context of globalization, in- 
duced by technology, through development policy, with public administrators at the reins. The 
policy makers steer economic development not unlike stagecoach drivers steer a team of horses. 
Public administrators and policy makers are at the reins of development. Their actions and deci- 
sions pilot, guide, and encourage or restrict the Economic Horsepower (EH) of an economy. 

Rostow (1991) used internationalization rather than globalization to discuss the process 
by which economies became interdependent. He brought elements of social overhead capital (in- 
frastructure) together with economics in the Stages of Economic Growth, which provided criteria 
(e.g., policy, technology adoption, income inequality, external forces) to weigh the readiness and 
capacity for aggregate economic growth (1991). Sen (1988, 1997, 1999) added that the state's 
level of development and thus its capacity to tend to the human development (individual) needs 
of its citizens is a key factor in the inequality formula, where capacity is the measure of total po- 
tentiality, whether actual or merely possible. North (1994, p. 17) underscores the necessity for 
well-informed policy decisions to manage the "unanticipated consequences and outcomes of de- 
cisions made in the face of uncertainty" a condition that is further constrained by the "limited 
capacity of humans to solve the complex problems" (North, 1994, p. 19; H. Simon, 1972). The 
research questions are studied in light of globalization, as the force of globalization adds an un- 
dercurrent of involuntary activity from seemingly exogenous sources and a theme of necessity to 
the balance of the literature. Understanding the elements of this debate is critical to informing 
development policies that mitigate growth's potentially negative consequences (Sen, 1999). 



Problem and Research Approach 

The IMF reports that "[c]orruption . . . diverts public resources to private gains, and away 
from needed public spending on education and health. . ..[and], by reducing tax revenue . . .it can 
accentuate income inequality" (IMF, 201 Id, p. 1). However, gaps exist in the literature on certain 
measures of corruption's effects on public education budgets, and on certain measures of the di- 
minished budgets on individual incomes. Further, it seems plausible that diminished education 
funding affects certain measures of individual income. Kuznets referred to the ability for an indi- 
vidual to earn income in part, his education and skill training as "the reverse side of income" 
(1934, p. 7). Yet, supporting evidence of corruptions effects on education budgets and income 
has lacked careful attention. 

Hence, the overarching hypothesis this thesis tests is that the effects of governance cor- 
ruption on education through the public resource mechanism, its budget, are direct and negative; 
we posit that the higher the degree of corruption, the lower the relative education budget. Fur- 
ther, the lower the education budget per capita, the lower the relative individual income. The 
combined weight of just these two effects of corruption on long-run economic growth is poten- 
tially debilitating. 

The measure or degree of governance corruption in a country and its effect on the func- 
tioning of that country is corrupt by its very nature. In order to measure corruption's effect on a 
government function such as its development policy, its economy, or human development, a 
country must first account for it on its balance sheet. It must measure the extent of the problem of 
corruption in dollars. 

Rent seeking, according to Tullock (1993, p. 2), is "the outlay of resources by individuals 
and organizations in the pursuit of rents created by government." By reducing the public resource 
pool, the remuneration for corruption could shift toward that which complements the endeavors 



of those that are corrupt and away from approved, sustainable economic development and mar- 
ket-demanded goods and services (S. Gupta et al., 1998). 

"State capture is any group or social strata, external to the state, that exercises decisive 
influence over state institutions and policies for its own interests against the public good" (Pesic, 
2007, p. 1). Essential to this thesis, IMF scholars Mauro, Abed et al. (2002, p. 278) assert that ex- 
torting from education starts before the budget approval, so fewer dollars are allocated to 
education, and more are allocated to projects where that extortion is easier to hide. 

An initial inquiry into relationships among and between governance, corruption, econom- 
ic development, and individual income variables yielded the four conceptual challenges addressed 
above. The three problem themes follow. 

Problem One includes gaps in the recent literature specifically tying governance corrup- 
tion to the mechanisms through which the corrupt affect income inequality (deLeon, 1993; S. 
Gupta et al., 1998; S. Gupta et al., 2000; Rose-Ackerman, 1999b). The second problem includes 
difficulty measuring governance, corruption, and economic development at the aggregate and in- 
dividual levels (Galbraith & Kum, 2005; Schneider et al., 2010a). Solving the mechanism and 
measurement problems requires that we add to the working definitions of governance corruption, 
an explanation of the GDP and Income variables. 

Accounting for governance corruption requires adding Official and Unofficial compensa- 
tion. Compensation On the Books adds to a country's official GDP through National Income 
Accounting (Kuznets, 1934). Remuneration On the Ground avoids official ledgers, and creates or 
adds to the unofficial economy, or Shadow Economy (SE). The Shadow Economy is defined as 
remuneration generated through actions and transactions representing primarily tax, regulation, 
and administrative process avoidance (Schneider et al., 2010, p. 5). The Shadow Economy is a 
"situation where businesses operating outside the tax system and registered businesses conceal 
transactions to avoid paying taxes or social security charges, or to avoid the costs associated with 

10 



legislation on safe working conditions or protection of consumers' rights" (Russell, 2010, p. 10). 
The Shadow Economy includes rent seeking and state capture, which are discussed later. 

Governance is restricted to the purview of formal, official governmental institutions at 
the national level plus the informal or unofficial institutions (North, 1991a) or "the traditions and 
institutions by which authority in a country is exercised" (Kaufmann, 2006, p. 82). The informal 
institutions may include officially recognized entities such as labor unions, and unofficial entities 
such as cartels. The scope of the informal institutions include the IGO, NGO, and business com- 
munities as they engage in transactions and the democratic process (Mauro et al., 2002). Other 
types of governance or management (e.g., corporate governance, business, institutional, or organ- 
izational management), while essential, are outside the scope of this thesis except to classify the 
productivity of goods or services as official or unofficial. History provides much evidence that 
governing regimes produce a spectrum of economic development results, some not so good. 
Armstrong (2005) asserts that good governance is a by-product of sound public administration 
and strong governmental institutions; it minimizes corruption and reinforces healthy and sustain- 
able economic development. Good governance, by definition, requires integrity, transparency, 
and accountability in the public sector (pp. 1-2). Conversely, poor or weak governance lacks the- 
se characteristics; it mushrooms out of corruption and maladministration, carrying with it 
devastating human costs (e.g., poverty, inequality, ill health, illiteracy) and a "lack of public trust 
that undermines and even destroys political stability" (p. 9). 

Problem Two is the measuring corruption per se (Kaufmann, 2006, p. 82). However, re- 
cent economic and statistical modeling has provided increasingly reliable approximations of its 
influence and economic costs through surveys, extensive audits and tracking of markets that are 
clandestine, extrapolation aided by increasing statistical capacity, and redundancy over time (p. 
82). The IMF uses the portion of total production attributed to the unofficial economy as a proxy 
for the level of governance corruption (Abed & Gupta, 2002b; Russell, 2010; Schneider, 2009; 

11 



Schneider et al., 2010). This thesis follows the IMF's lead, using the definition and measurement 
devised by Schneider & Enste (2000), for the unofficial or Shadow Economy, as the proxy for the 
corruption found in governance. 

Measuring economic growth's effect on the individual income, economic condition, and 
living standard requires a standardized measure. Sen addressed these elements by conceiving the 
Human Development Index (HDI) and subsequent Human Development Reports (HDR) from 
1990 through present. A living standard is characterized, in part, by an individual's insufficient 
means to earn a living, particularly by insufficient education or skill and the access to basic needs 
(HDR, 2009e). Healthy and sustainable economic growth is a byproduct product of a healthy 
economy, which is maturing, growing, tending to the human development and thereby capacity 
development needs of its population, and is not likely to exacerbate poverty rates or levels 
(Kuznets, 1971; Thomann, 2008). 

Measuring economic growth accurately and adequately spotlights limitations Kuznets 
knowingly built into the National Income accounting system, still used by researchers and policy 
makers throughout the world today to report data used internally by country and externally to in- 
ternational agencies. 

Economic welfare cannot be adequately measured unless the personal distribu- 
tion of income is known. And, no income measurement undertakes to estimate 
the reverse side of income, that is, the intensity and unpleasantness of effort go- 
ing into the earning of income. The welfare of a nation can, therefore, scarcely 
be inferred from a measurement of national income as defined (Kuznets, 1934, 
pp. 6-7). 

Kuznets' "intensity and unpleasantness" (p. 6) represents a spectrum of types of effort. 
Effort ranged from the required physical toil to the dexterity to manage the necessary motivations 
and the overall economy. Likewise, the effort ranged from the mental muscle required in policy 
learning to the earning of a degree. 

Kuznets' distribution of personal income is quantitative and the "effort going into the 
earning of income" is qualitative (p. 7). GDP data, together with other economic indicators, pro- 

12 



vides sufficient material to replicate Kuznets' original tests using new data. Yet, even significant 
results from testing income distribution in the equation reaches not half of the essence of his as- 
sertion. Capturing and measuring the "intensity and unpleasantness of effort going into the 
earning of income" (pp. 6-7) is at the heart of Sen's work on human capability and is central to 
his human development research (1997, 1999, 2004). It adds valuable context to Kuznets' "re- 
verse side of income" (1934, pp. 6-7). The HDI measures economic development holistically by 
measuring the development of its citizens' capability to earn and live. By doing so, the HDI rec- 
ords a measurement by which policy analysts can evaluate development policy (HDR, 2009e; 
Alkire, 2005; Mazumdar, 2003). 

Problem Three is the scope and limitations of this thesis. Important to note here is that 
many variables that are widely used in cross-country analysis on economic growth and individual 
income in transition countries are beyond the scope of this thesis. Specifically, future research 
would include three important variables. (1) A variable critical to economic productivity would 
measure progress toward market liberalization (Sachs & Werner, 1995). In the 2010 Transition 
Report, the EBRD offers a "new sector-based approach to measuring transition progress" that 
provides data on privatization, markets, banking, and infrastructure (p. 3). (2) Progress toward 
EU accession, measured by the European Commission (201 lb). (3) A variable critical to under- 
standing economic growth patterns would mark the history and intensity of armed conflicts 
(HIIK, 2010a). Variables that indicate market liberalization, EU accession progress, and periods 
of unrest may add explanatory power to the analysis. 
Sequence of Reasoning 

Step One in the sequence of reasoning is to create a baseline or starting point that 
measures the accumulated stock of human development in each country in 1990. The Human 
Development Index (HDR, 1990) equally weighs three observations: the Life Expectancy Index 
(LEI), the Educational Attainment Index (EAI), and the GDP Index (GDPI). This baseline meas- 

13 



ure becomes the pre-test, independent variable, HDI 1990 . Data are available for each of the coun- 
tries in the sample set. We test to ensure that the HDI baseline of the sample data are consistent 
with widely accepted findings, where the GDP per capita is not a statically significant proxy for 
the living standard or human development, consistent with Galbraith & Kum (2005), Sen (Sen, 
1984), and Kuznets (1934), among others. Research question one correlates to step one in the 
logic sequence. 

Research Question 1 : Are the Human Development Index and Income per capita highly 
correlated? If GDP per Capita is not a sufficient proxy for the stock of human development, we 
can move forward with the next step. 

Step Two measures governance corruption's effects on GDP per Capita, or Individual In- 
come per Capita (Ic). Some scholars argue that all of the accepted income measurements lack 
accuracy due in large part to the inaccuracy of the production data going into them {e.g., Gal- 
braith & Kum (2005); Schneider & Enste, (2000)). Even the best GDP data report only the 
income earned on production of goods and services that are accounted for On the Books in the 
National Income Accounting system Kuznets designed (1934, ch. 1). The balance of the remu- 
neration moves through the unofficial economies (Abed & Gupta, 2002b; Schneider & Enste, 
2000). The notation for aggregate income On the Books, or Official Individual Income is Ic . 
Likewise, estimated corruption remuneration earned On the Ground, the Unofficial Individual In- 
come per Capita, is Ic n . Adding the two streams of productivity together approximates the total 
value of goods and services produced in a country in one year, Total Individual Income, I + Iu = 
I T (Galbraith & Kum, 2005). (See Data Legend in Appendix). Research question two corre- 
sponds to step two in the logic. 

Research Question 2: Does governance corruption, as measured by the Shadow Econo- 
my, negatively affect Income per Capita? 



14 



Step Three's purpose in the sequence is to uncover the relationships among and between 
the elements of corruption defined by the Shadow Economy and education funding. In the offi- 
cial National Income Accounting for GDP is an allocation for public education funding. The of- 
ficial government's budget line item, Education Expenditure as a Percentage of Total Govern- 
ment Expenditures (EE), reflects an education target set forth in that country's economic 
development policy. In Armenia from 1999 to 2007, for example, the official expenditure on 
public education averaged 2.52 percent of the GDP. This translates to 13.2 percent of the total 
government expenditure (UNESCO, 2009d,Table 13). Research question three corresponds to 
logic step three. 

Research Question 3: Does governance corruption, as measured by the Shadow Econo- 
my, negatively affect Education Expenditure? 

Step Four is the key research question for this thesis. It sets up the reasoning to test the 
Ic, HDI, SE, and EE variables simultaneously. Nelson and Phelps write, ". . .educated people 
make good innovators, so that education speeds the process of technical diffusion (1966, p. 70). 
According to Romer (1994b), Arrow (1962), Lucas (2009), and others, education's unique contri- 
bution to economic growth is increasing returns or knowledge spillovers, which sets education's 
contribution apart from other public goods (e.g., infrastructure, health, defense, etc. . .) and from 
other economic growth factors (e.g., consumption, savings and investment, trade balances, tax 
consequence, etc. . .). For a country and its citizens, education is an investment in future econom- 
ic sustainability. For a Shadow Economy and its constituents, an educated citizenry may be 
threatening (Monas, 1984). Hence, Education Expenditure budget provides a logical line item, 
though certainly not the only line item, from which to direct public funds (Freire, 1970). Re- 
search question four corresponds to logic step four. 

Research Question 4: Do the pre-test Human Development Index, Governance Corrup- 
tion, and Education Expenditure together explain the change in Income per capita? 

15 



Step Five is to define the parameters of the sample data set. 

Sample Set Rules 
Rule 1 : The country was or remains Socialist 

Rule 2: Four or more years of Soviet influence (Sachs & Warner, 1992, 1996, 1998), plus a cre- 
ated, liberated, or re-gained sovereignty, independence or the ability to trade, travel, and migrate 
which began between 1988 and 1992. 

Rule 3: Geographically related by inland border, trade or sea-trade route, and western oriented. 
Rule 4: Ethnolinguistically interrelated, Economically interdependent 



16 



Thesis Overview 
The purpose of this thesis is to explore the intricacies of the effects of governance corrup- 
tion (measured by the Shadow Economy) on individual economic growth (measured by Ic) 
through education budgets (measured by Education Expenditures on public education as a per- 
centage of GDP). Chapter 2 reviews segments of the major bodies of literature that are 
specifically relevant to governance, corruption, and human and economic development to frame 
the thesis, starting with broad academic themes and theories, and narrowing to the specific works 
upon which this thesis is built. In doing so, the work outside the scope of this thesis is excused 
and the reasons for this are discussed. This segment adds context to the data. Chapter 3 explains 
and examines these data, describes logical flow of the equations for analysis, and discusses each 
step in the econometric methods with which the data are analyzed. Chapter 4 presents the results, 
describing complications and resolutions to data or methodological issues. Chapter 5 offers an 
analysis of the econometric results and discusses the findings in terms of the framework, specific 
theories, and the model employed, as well as necessary caveats and limitations of the data. Chap- 
ter 6 describes some potential next steps for the policy community and suggests future research 
scholars may undertake to further the thesis findings. The Appendix in includes the Country 
Brief, in which is found data on each of the countries in the sample set relevant to governance, 
corruption, and human and economic development. 



17 



CHAPTER 2 

LITERATURE REVIEW 

The goal of this dissertation is to inform economic development policy to encourage 
healthy economic growth (Kuznets, 1966, p. 493; Sen, 1997) using the New Growth Theory that 
is widely accepted in cross-country longitudinal analysis (Cortright, 2001b; Romer, 1996). Clear- 
ly a proponent, Romer (1994a, p. 21) asserts that, "The most important job for economic policy is 
to create an institutional environment that supports technological change." The following litera- 
ture review takes advantage of New Growth Theory's treatment of development policy and 
knowledge capital, which is characterized by increasing returns, and which is required to adopt 
and exploit technological advance (Phelps & Nelson, 1966; Romer, 1993). New Growth Theory 
bridges the two main theories on economic growth, which lie on a continuum from endogenous 
and exogenous drivers. The incentives provided by national governance via the development pol- 
icy process bridge endogenous growth motives and exogenous knowledge catalysts. The major 
bodies of literature reviewed are governance and corruption as a dimension of governance, eco- 
nomic growth and economic development, human development and its components, and theories 
about and measurement of education outcomes. 



Governance 

According to the IMF (201 Id, p. 1), "[governance is a broad concept covering all as- 
pects of the way a country is governed, including its economic policies and regulatory 
framework." The IMF definition frames the concept by identifying the level, manner, and system 
of authority. This framing permits a separation in the governance literature between dimensions 
that inform this thesis and those that do not. Pertinent are the forms of governance that directly 
influence national-level development policy specifically through its budget. Further, the influ- 
ence on governance through informal authority by any institution or organization is recognized 
and included. Corruption is, therefore, included, as it must be given that corruption is "informal" 
and influences governance. 

Kooiman & Jentoft (2009, p. 818) assert, "[t]he term governance has become a catchword 
in the social sciences as well as in the policy world. . . [and] has different meanings to different 
people." It is usually qualified by such terms as good, network, global, natural resource, or pub- 
lic, "while general theorizing on the concept remains rare." For example, Boviard & Loffler 
(2003, p. 316) define "public governance to be the ways in which stakeholders interact with each 
other in order to influence the outcomes of public policies." Stoker, in Governance as Theory 
(1998, p. 18), proposes aspects of governance. For example, governance "recognizes the power 
to get things done which does not rest on the power of government." The IGOs leading the storm 
of research activity on national and institutional governance each define governance slightly dif- 
ferently, according to the respective organization's need. The OECD, focuses (mostly) on 
governance between the national and sub-national public sector and private firms working within 
the public sector's domain (Towards Better Measurement of Government, 2007). The UN works 
with institutions at every level through programs worldwide focusing on the human condition and 
poverty elimination (1985), and peace keeping. The IMF promotes soundness and transparency 
in banking and financial management (201 Id). The World Bank focuses on strengthening the 

19 



economic development ability of national -level institutions and governments through governance 
norms and generating aggregate data for all nations (2009g). The scope, breadth, and depth of 
literature on governance are thus, enormous; however, the dimensions of governance central to 
this thesis present a narrow spectrum. The following section defines the boundaries between 
governance literatures by their specifications to include or excuse them from a role in this thesis. 

North's (1991a) work on governance combines the formal government plus the institu- 
tions as actors that command respect in the economy. Institutions, considered broadly, are, "the 
humanly devised constraints that structure human interaction— the 'rules of the game'" (North, 
1994, p. 8). Informal institutions permeate the culture in a variety of forms (e.g., a cultural norm, 
NGOs, the local PTA, unions, family movie night, gangs, mafias, industry associations, charities, 
churches, holidays, lobbyists, and political action committees), and may have positive, neutral, or 
negative impacts on government's formal policy objectives. Here, governance is restricted to the 
purview of (1) formal authority of governmental institutions and (2) informal authority or influ- 
ence by institutions (North, 1991a) that affect national -level government budget, taxation, 
spending, fiscal, or monetary policy. The scope includes governance over certain actions and 
transactions by official, institutional, and private organizations or their respective individual ac- 
tors. 

To limit the breadth of governance, forms of governance, or management, which serve to 
manage or direct the mterworking of private-sector operations (e.g., business, institution, enter- 
prise, company, charity) or Non-Governmental Organizations (NGOs) and associations, are 
outside the scope of this thesis (Kettl, 2000). 

The depth of governance relates to the level at which governing takes place. Global gov- 
ernance, for example, focuses on "updating the existing multilateral institutions, and creating an 
effective oversight body . . .to bring together national government, multinational public agencies, 
and civil society to. . .address global challenges" (Boughton & Bradford Jr., 2007). Susan Rose- 

20 



Ackerman proposes an international "tribunal" or other form of multinational non-governmental 
organization (Rose-Ackerman, 1999a, p. 195). The option of governing from the highest level 
seems unlikely to gain traction as global powers such as the US and EU seek to fortify autonomy 
through leadership, alliance, and example (Armitage & Nye, 2007; Kaminski, 2005), and have 
more readily supported the governance strengthening efforts within a national or regional scope. 
Hence, a global perspective is outside the scope of this thesis. 

The other extreme limits the scope of governance research to that of only a national gov- 
ernment's formal influence. This is equally unrealistic as a method to solidify good governance 
as this neglects the influence of the private sector, institutions, and the IGO and NGO communi- 
ties in particular (North, 1991a). On the contrary, informal authority by any of these entities, 
listed above or tacit, is within the scope of this thesis, if the influence affects national-level eco- 
nomic development policy (i.e. through variation, adaptation, modification, or transmutation 
within the development policy process or within the fiscal or monetary policy processes), eco- 
nomic growth, sustainability, or economic outcome. 

To limit the depth of governance, levels of governance above the national level {i.e., in- 
ternational agencies such as the UN, and intergovernmental organizations such as the European 
Union) or beneath the national level {e.g., non-national, regional, territorial, municipal, tribal) that 
serve to manage or direct the mterworking of non-national-level governments, are outside the de- 
fined scope of this thesis. 

An understanding of a country's governance must capture the method by which the gov- 
ernment system governs. Government systems exist on continuum from centralized to 
decentralized decision-making and control, adding another dimension to the concept of govern- 
ance. Governance literature exposes "the relationship between state intervention and societal 
autonomy, (and). . .different strands of the literature highlight different facets of this continuum. 



21 



Existing understandings may be classified according to whether they emphasize the politics, poli- 
ty or policy dimensions of governance" (Treib et al., 2007, p. 4). 

To summarize, the word governance refers to the system, structure, and form of govern- 
ment including its actors, as well as the act of governing or the method by which a system is 
governed. Governance includes the scope and breadth of influence, official or unofficial, that af- 
fect the legitimate public sector, institutional, political, and policy systems (North, 1991a; 
deLeon, 1993; Mauro, 2004; Kaufmann et al., 2008) and command respect and allegiances in so- 
ciety and, therefore, on the economy itself (Rose-Ackerman, 1978, 1999; North, 1991a). 
Governance actors include public and private institutions, public officials, and private citizens 
transacting in the democratic process (Thompson, 2007, p. 2). 
Measuring Governance 

Governance scholars seek to understand, to measure and, to measure the impact of both 
the formal and the informal authority in a given society if it were to be able to manage its system 
for the greater good of the whole nation (Abed & Gupta, 2002b; Kaufmann et al., 2000; Treib et 
al., 2007). However, measuring an intangible (such as transparency in governance) is an elusive 
matter of perception, and the task, enormous. World Bank's governance scholars created a meth- 
odology for the World Governance Indicators (WGI) metric (Rose-Ackerman, 2006). "The 
indicators are based on several hundred individual variables measuring perceptions of govern- 
ance, drawn from 35 separate data sources constructed by 32 different organizations from around 
the world" (Kaufmann et al., 2008, p. 1). The aggregated score, or index, is used as a meter of 
governance "process. . .capacity. . .(and) respect" (p. 7). The six dimensions of governance are 
Voice and Accountability, Political Stability and Absence of Violence, Government Effective- 
ness, Regulatory Quality, Rule of Law, and Control of Corruption (Kaufmann et al., 2008). 

Similarly, UN agencies such as the United Nations Development Programme, measure 
participation, consensus orientation, accountability, transparency, responsiveness, effectiveness 

22 



and efficiency, equitability, and rule of law (2009f). The UN adds more survey information gath- 
ered from the same and other country-level agencies as the WGI to augment the data for a gov- 
governance index suitable to UN needs, which reflect sub-national governance factors (What is 
Good Governance?, 2009). Therefore, the UN data are not sufficient for this thesis. The IMF 
concentrates its research on governance of financial institutions rather than national-level gov- 
ernmental governance, and is not appropriate for this thesis (IMF, 201 Id). 

Critics are quick to list the limits to and faults in the available governance indices. 
"Tackling the issue of measuring governance was the premise of a meeting of scholars, data ex- 
perts, clients, donors, and policy makers at the Kennedy School of Government, Harvard 
University, in May 2003" (Besancon, 2003, p. 1). 

The World Bank's Worldwide Governance Research Indicators Dataset, the 
Global Governance Initiative, the OECD's Participatory Development and Good 
Government rankings, Freedom House's index, and Transparency International's 
rating system for governance are all primarily subjective, being based on expert 
or informed opinions, systematically gathered and arrayed with or against other 
perceptions and surveyed views. So are the majority of the forty-seven data sets 
[that are available]. . ..Data experts inform us that one of the simple reasons for 
using subjective data is that no complete cross-country, objective data are availa- 
ble, particularly from the underdeveloped nation states. 

Answering the critics, efforts to complete objective research are underway. Based on a 
successful pilot study, using field researchers collecting data from a sample of countries that pro- 
vided information-rich data, Robert Rothberg (2005) asserts that the time has come for a 
quantitative measure of governance. Until such a project is complete, he suggests an ordinal 
ranking of countries, an index, to bolster the qualitative data, following the lead of the WGI, Hu- 
man Development scholars and others. Johnston (2007, pp. 8-9) put forth a benchmarking 
strategy that emphasizes integrity in government processes; however, like that of Rothberg, this 
effort is also time and resource intensive. Most critics agree that the WGI is the most widely ac- 
cepted index, largely due to the funding provided by the World Bank for developing it, and for its 
inclusivity of non-Bank scholars, national, and international agencies (Bovaird & Loffler, 2003; 

23 



Kurtzman et al., 2004). Radelet (2003, p. 33) writes, "[t]he most comprehensive set of global 
governance indicators has been compiled by the World Bank and combines subjective and objec- 
tive attributes." Absent completed objective research, however, and because field research is 
limits to a handful of countries, these sources are insufficient. Instead, this thesis employs data 
that estimates the budget effects of governance, discussed further in the section Measuring Cor- 
ruption (Schneider et al., 2010a). 

Moving on, while the international agencies have a unique governance foci (e.g., IMF, 
Bank, OECD, and UN), each stress the criticality of good governance to foster sustainable eco- 
nomic growth and development, and inclusive prosperity. The distinction made between 
governance and the characteristics of good governance is central to this thesis. As Kaufmann, 
(2000, p. 1) state, "...there is a strong causal relationship from good governance to better devel- 
opment outcomes such as higher per capita incomes, lower infant mortality, and higher literacy." 

In this thesis, a two-equation simultaneous linear regression model, the Multiple Indicator 
and Multiple Causes (MIMIC) equation to estimate and measure governance as a missing varia- 
ble. This equation measures the quality of governance by the percentage of corruption and 
underground activity in the economic process simultaneously (Breusch, 2005, p. 5). Given that 
GDP is the standard measure for an economy (the argument for this is developed in the literature 
review section on economic growth), the estimated percentage of simultaneous corruption and 
underground activity missing from the GDP is the inverse of the quality or 'goodness' of govern- 
ance (Schneider et al., 2010a). 



Good Governance and Sustainable Development 

According to Rotberg (2009, p. 113) "[governance is the delivery of political goods to 
citizens. The better the quality of that delivery and the greater the quantity of the political goods 



24 



being delivered, the higher the level of governance. ..." Over time, civilizations have thrived, 
prospered, deteriorated, and dissolved for many reasons beyond the scope of this thesis. Despite 
this waxing and waning of civilizations and nation states, and essential to this thesis, however, is 
clear evidence of a general accumulation of capability such as economic and human capability, 
commerce, longevity, learning, and technology, all of which were subject to (or because of) the 
prevailing system of governance. Grindle (201 1) describes a new theme in governance literature. 

This common theme suggests a new generation of thinking that emphasizes the im- 
portance of knowing the context in which reformed policies, institutions, and processes are to be 
introduced, and designing interventions that are appropriate to time, place, historical experience 
and local capacity. ... Understanding the historical evolution of how countries muddle their way 
toward relatively efficient and effective institutions is critical. ... (p. 415). 

Many scholars have sought to measure the stock of capability, inherited or earned, as an 
indicator of the quality or the degree of goodness of governance. For example, Elisseeff (1998) 
studied the Silk Road trade routes, which fostered a mixing of races and cultures, as well as the 
building of vast realms, such as the Mongol, Roman, and Persian empires. Language, culture, 
ethnicity, heritage, beliefs, religions, customs and philosophies, migration patterns, trade routes 
and knowledge diffusion, geography and climate, political arrangements and government struc- 
tures, and pure blind luck, among other historical human and institutional factors together, affect 
and are affected by governance (Diamond, 1997; Elisseeff, 1998; Mauro, 1995). These are ele- 
ments of governance, but neglect a measurement of governance, and offer an insufficient metric 
for this thesis. 

However, to accommodate the diffusion of cultures and institutions, scholars developed a 
measurement for the degree of ethnic, linguistic and religious homogeneity, heterogeneity, or 
what they term f rationalization, and its effects on economic growth (Alesina et al., 2002). Mau- 
ro (1995) used an index of ethnolinguistic fractionalization, as an instrument to account for the 

25 



homogeneity as an indicator of cultural behavior, language based, and non-language based com- 
munication barriers. La Porta & Lopez-De-Silanes (1999, p. 223) used the opposite measure, 
ethno linguistic heterogeneity, to inform "theories of determinants of institutional - and more spe- 
cifically government performance" (p. 233), on the importance of economic, political, and 
cultural historical factors. They asserted that poor countries, closer to the equator, that "use 
French or socialist laws, or have high proportions of Catholics or Muslims, exhibit inferior gov- 
ernment performance" (p. 222). Good government performance, to these scholars, is, "good 
economic development" (p. 223). Ethnolinguistics is important in governance research. 
"Ethnoliguistically homogenous countries have better governments than heterogeneous ones. 
Common Law countries have better governments than French civil laws or socialist law coun- 
tries. Predominantly Protestant counties have better governments than [do] either predominantly 
Catholic or predominantly Muslim countries" (1999, p. 265). Moreover, those with a history of 
British rule with Protestant traditions seem to be less corrupt (Serra, 2006; Treisman, 2000). 

Fractionalization is adopted in this thesis through the proxy variable for corruption, the 
Shadow Economy, as it is one of the inputs employed by Schneider et al. (2010). Wars and holo- 
causts greatly affect its index value, as holocausts decrease a country's fractionalization by 
exterminating a segment of ethnic or religious groups (Burleigh, 1996). Following is an example 
of the effect a fractured population has on growth (Alesina et al., 2002, p. 9). 

In terms of economic magnitudes, the results. . .suggest that going from complete 
ethnic homogeneity (an index of 0) to complete heterogeneity (an index of 1) de- 
presses annual growth by 1.9 percentage points. In other words, up to 1.77 
percentage points of the deference in annual growth between South Korea and 
Uganda can be explained by deferent degrees of ethnic fractionalization. 

According to Paulo Mauro (1998a, p. 266), "[fjhis variable is a good instrument because, 
in accordance with Shleifer and Vishny (1993) arguments, more fractionalized countries tend to 
have more dishonest bureaucracies. The index of ethnolinguistic fractionalization has a correla- 
tion coefficient of .36 (significant at the conventional levels) with the corruption index." Mauro 

26 



(1998a, p. 266) added a proxy for the degree of state capture by Ades and Di Telia (1994), a 
proxy for the degree of rent-seeking "following arguments by Sachs and Warner (1995)", and 
"whether the country achieved independence after 1945 (following Taylor and Hudson, 1972)." 
The correlation coefficients between these and corruption index were .21, .23, and .41, respec- 
tively (Shleifer & Vishny, 1993). 

Barro & McCleary (2003), following North (1994), measure belief systems as a determi- 
nant of governance. Furthering work by Max Weber (1930, pp. 22-27), La Porta et al. (1999) use 
religion as "a proxy for work ethic, tolerance, trust, and other characteristics of a society that may 
be instrumental in shaping its government" (p. 224). The key finding as it pertains to this thesis is 
this: "Statist laws are thus a more robust predictor of poor government performance than inter- 
ventionist religions" (p. 264) or cultural influences (p. 224). Therefore, a country's 
fractionalization, its political, legal, economic, religious, and cultural history, its age as an inde- 
pendent state, and the influence of unofficial institutions through rent-seeking and state capture, 
are posited to be critical influences in and on governance, yet taken together are insufficient to 
measure governance directly. 

Economic geography, which explains concentrations of economic activity based on geo- 
graphic space, adds an understanding of the role of global latitude and climate in the economic 
growth (Diamond, 1997; Krugman, 1998; Nissan, 1991). Scholars continue to work on modes 
and methods of governance delivery in an age of rapid information dissemination to foster an in- 
creased understanding of how the power of institutions determines the quality of governance 
(Kersley et al., 2008; Kettl, 2000; Lynn, 1998; Treib et al., 2007). Heritage, migration, geograph- 
ic proximity, and the diffusion of races and knowledge all inform the notion of governance, but 
do not measure it adequately. 



27 



Human Development as a Metric for Governance 

Building off Kenneth Arrow's work in Social Choice Theory (Arrow, 1963a, 1963b), 
Sen's seminal work in welfare economics laid much of the foundation for the body of literature in 
human development. Sen's Inequality Reexamined (1992), Human Capital and Human Capabil- 
ity (1997), and Development as Freedom (1999), provide the framework for using the Capability 
Approach as an indicator of prior governance, and to measure governance (Alkire, 2005). "Per- 
haps most importantly, the human development approach has profoundly affected an entire 
generation of policy-makers and development specialists around the world" (HDR, 2009e, p. iii). 
Working with United Nations Human Development Programme (UNDP), Sen and Huq devised 
the Human Development Index (HDI). The HDI is an indexed value for the accumulated store of 
a country's human capital and human capability. This stock of capability is the summation of the 
whole of a country's history; that includes the effects of ethnic, linguistic, and religious fraction- 
alization, the geography, travel routes, trade agreements, regime changes and wars, latitude, 
climate, plagues, and holocausts, and luck (HDR, 1990; Sen, 1997). 

The human development index is a composite index that measures the average 
achievements in a country in three basic dimensions of human development: a 
long and healthy life, as measured by life expectancy at birth; knowledge, as 
measured by the adult literacy rate and the combined gross enrollment ratio. . . 
and a decent standard of living, as measured by gross domestic product (GDP) 
per capita in purchasing power parity (PPP) US dollars [in 2007 as the base year] 
(HDR, 2007, p. 225). 

Scholars collaborate and share research and data through networks such as the Interna- 
tional Comparison Program (ICP) and Eurostat's Statistical Data and Metadata Exchange 
(SDMX), delivering data sets for internal and public use. These "state-of-the-art measures incor- 
porate recent advances in theory and measurement and support the centrality of inequality and 
poverty in the human development framework.. . .with the intention of stimulating reasoned public 
debate beyond the traditional focus on aggregates" (HDR, 2007, p. 224). 



28 



Importantly, the HDI normalizes its data across countries in two ways. First, using pur- 
chasing power parity, the value is normalized. Second, using GDP per capita shifts the focus to 
the individual as the unit of measure, rather than looking at country aggregates. A feature of the 
HDI is that its particular component indices capture a sense of community cohesion, the level or 
degree of social capital (Carilli et al., 2008), to aid in approximating the country's bounded poten- 
tial, as well as that of the individual (HDR,1990; Sen, 1997; H. A. Simon, 1997). 

Well defended as a goal in and of itself, human development directly enhances the capa- 
bility of people to lead worthwhile lives (Sen, 1997), so there are immediate gains in what is 
ultimately important, while safeguarding similar opportunities for one's neighbor, and for the fu- 
ture. A country that enjoys high human development, such as the United States, has nearly 
limitless 'boundaries' on its potential for innovation and progress on many fronts from military to 
the arts. Citizens who suffer from low literacy rates, poor health, and extreme underemployment, 
in a country scoring low on the HDI, are effectively bounded by today's struggle for food and 
shelter (Sachs, 2005; H. Simon, 1972). Others utilize similar variables to gauge governance qual- 
ity (La Porta et al., 1999). "We measure the output of public goods by infant mortality, school 
attainment, illiteracy, and [by] an index for infrastructure quality" (Anand & Sen, 2000, p. 237). 

There is hardly any example in the world of the expansion of education and 
health being anything other than monotone: good education and good health 
seem to generate powerful demand for these opportunities (and more) for our 
children. This is a relationship that goes well beyond the redistribution of in- 
come to the poor at a given point of time important though that is. It should also 
be noted that any instrumental justification for human development. . .relates con- 
cretely to people's ability to generate for themselves the real opportunities of 
good living (p. 237). 

Sen wrote the following in the Forward for the 2009 HDR. "In 1 990 public understand- 
ing of development was galvanized by the appearance of the first Human Development 
Report. . ..it had a profound effect on the way policy-makers, public officials and the news media, 
as well as economists and other social scientists, view societal advancement" (p. iv). "While the 
concept of human development is much broader than any single composite index can measure, 

29 



the HDI offers a powerful alternative to income as a summary measure of human well-being" 
(HDR, 2007, p. 225). For the reasons cited above, and, because the HDI is an amalgam of com- 
ponent indices that approximate the entirety of development attained by the respective country 
and year (HDR, 1990), the HDI is the best measure available for the stock of development and 
governance. For this thesis, these factors are the stock of capability in the pre-test year HDIi 99 o. 

To summarize, governance refers to the system, structure, and form of government in- 
cluding its actors, as well as the act of governing or the method by which a system is governed. 
For the purposes of this thesis, governance is limit to the influence, official or unofficial, that af- 
fects the legitimate, national-level, public sector, institutional, political, and policy systems. Its 
actors include public and private institutions, public officials, and private citizens transacting in 
the democratic process. Since its influence commands respect and allegiances in society and 
among its actors, governance affects the economy. 



30 



Corruption as a Dimension of Governance 
The social sciences literatures qualify corruption by type or characteristic. Moody-Stuart 
(1996, p. 19) uses the definition for corruption found "in the Encyclopedia of the Social Sciences: 
Corruption is the misuse of public power for private profit." He "distinguish[es] between "grand 
corruption," which involves senior officials, ministers, and heads of state, and "petty corrup- 
tion",. . .which is usually about getting routine procedures followed more quickly." Emphasizing 
the criticality of the difference, he states the following. "But grand corruption can destroy na- 
tions: where it is rampant, there is no hope of controlling petty corruption" (p. 19). It is the 
effects of 'nation-destroying' corruption that we seek to understand and measure in this thesis. 
Joseph Nye (1967, p. 419) defines and characterizes corruption in the following way. 

Corruption is behavior which deviates from the formal duties of a public role be- 
cause of private -regarding (personal, close family, private clique) pecuniary or 
status gains; or violates rules against the exercise of certain types of private re- 
garding influence. This includes such behavior as bribery (use of a reward to 
pervert the judgment of a person in a position of trust); nepotism (bestowal of 
patronage by reason of ascriptive relationship rather than merit); and misappro- 
priation (illegal appropriation of public resources for private -regarding uses). 

Nye continues by stating that corruption may be beneficial to economic development, 
governmental capacity, and institutional integration into the political arena. As such, increasing 
its transparency and legitimacy, or authorizing those aspects beneficial to society and public wel- 
fare may be the way to ameliorate corruption (pp. 419, 427). 

While these definitions are widely accepted, each limits the scope of corruption to a polit- 
ical realm or public sector, which will not do for the purposes of this thesis. Robert Merton, on 
the other hand, suggests that corruption may be a remedy created by society; "functional deficien- 
cies of the official structure generate an alternative (unofficial) structure to fulfill existing needs 
somewhat more efficiently" (1968, pp. 127, emphases in original). We seek to measure the ef- 
fects of corrosive, development -limiting corruption in this thesis, whether public or private, if it 



31 



alters the composition of government expenditure by avoiding the democratic process or by influ- 
encing budgets or spending (Mauro et al., 2002, pp. 263-265). 

Governance corruption refers to corruption in the system, structure, and form of govern- 
ment including its actors, as well as the act of governing or the method by which a system is 
governed. Its actors include national-level public and private institutions, or public "officials and 
private citizens who pursue private interests by circumventing the democratic process" (Thomp- 
son, 2007, p. 2). Therefore, in this thesis, governance corruption includes the scope and breadth 
of influence, official or unofficial, that affect a distortion into the legitimate, national-level, public 
sector, institutional, political, and policy systems (North, 1991a; deLeon, 1993; Mauro, 2004; 
Kaufmann et al., 2008) that command respect and allegiances in society and, therefore, on the 
economy itself (Rose-Ackerman, 1978, 1999; North, 1991a). 

Examples of inferior, as opposed to good, governance raise the question about the cause 
of the failure in governance. Strictly defining corruption, however, forces bounds on the word as 
if corruption is merely a movement in character or form of a thing (or person) from point A to- 
ward point B. Instead, corruption is not linear, and it is more than one adjective can describe. 

Corruption is a process, thus, it must be viewed in its context. Corruption must be evalu- 
ated for its effects; measured, then re-examined for its causes - as a cyclical rather than linear 
movement. In context, corruption may be temporarily beneficial, as in composting to build nutri- 
ent rich soil. In context, corruption may be necessary to reach a mutually desired societal goal 
(Rose-Ackerman, 1999a). Corruption in the proper context may fill a societal need or promote 
economic development, and outside the proper context, may destroy a nation (Merton, 1968; 
Moody-Stuart, 1996; Nye, 1967). According to Nye (1967, pp. 419-422), corruption has poten- 
tial development benefit in three major categories: economic development, national integration, 
and governmental capacity. "If corruption helps promote economic development which is gener- 
ally necessary to maintain a capacity to preserve legitimacy in the face of social change, then (by 

32 



definition) it is beneficial for political development" (p. 419). Corruption may further economic 
development by the cutting of red tape, through capital formation, incentivizing entrepreneurship, 
and overcoming discrimination. Corruption may further governmental capacity, as well. "The 
capacity of the political structures of many new states to cope with change is frequently limit by 
the weakness of their new institutions and (often despite apparent centralization) the fragmenta- 
tion of power in a country. Moreover, there is little "elasticity of power" -i.e., power does not 
expand or contract easily with a change of man or situation" (p. 421. parentheses in original). 
Tanzi (1998, pp. 581-582) echoes Nye's assertions that corruption may benefit a development by 
citing "Tullock (1996) and Becker and Stigler (1974). . .Baumol (1990) and Murphy & Shleifer, 
and Vishny (1991)." 

In Gupta et al. (2000) we read the contradictory claims about the economic benefits of 
corruption. Some, consistent with Nye (1967), claim it is beneficial as method to "overcome and 
overly centralized and overextended bureaucracy, red-tape, and delays (Leff, 1964: Lui, 1985)" 
(p. 7). On the contrary, citing "Pradhan and Compos (1999) and Wei (1997) (p. 8)," as well as 
"(Kaufmann and Wei, 1999). . .and the 1997 World Development Report", Gupta et al. suggest 
the "efficient grease hypothesis is not supported by data" (2000, p. 7). While evaluating corrup- 
tion further is outside the scope of this thesis, defining and measuring corruption is necessary, and 
requires the reader to know its context. Part of corruption's context is its form and the level at 
which it operates. 
Forms, Size, and Scope of Corruption 

Corruption has many meanings in the literature. Corruption is a struggle between public 
good and rent-seeking individuals, and is a principal inhibitor of economic growth and economic 
development (Abed & Gupta, 2002a; deLeon, 1993; Heidenheimer & Johnston, 2002; Johnston, 
2007; Mauro, 1995; Rose-Ackerman, 1999b; Rotberg, 2005). In Thinking About Political Cor- 
ruption, deLeon (1993, pp. 23-25) offers several methods to categorize corruption. For example, 

33 



Lowi differentiates corruption based on its scale, where Corruption, "Big C," such as that in the 
Iran-Contra affair, is "often justifiable" by the participants, and requires coordination among 
many parties. Spelled with a little C, Lowi's corruption is small-scale scandal, '"that reflects or 
contributes to individual moral depravity' . . . [such as] embezzlement, tax evasion, or special privi- 
leges of office " (as found in deLeon, 1993, pp. 23-25). Heidenheimer colors corruption black, 
white, or gray, depending on the probability that a majority consensus would find the acts punish- 
able based on principle, tolerable, or meet mixed review based on the class or status, respectively 
(quoted in deLeon, 1993, pp. 23-24). Rose-Ackerman (1999b, p. 27) distinguishes corruption by 
its scale, as well, where '"Grand Corruption' occurs at the highest levels of government and in- 
volves major government programs and projects" providing examples such as cartels and 
privatization processes rife with bribery (Rose-Ackerman citing Moody-Stewart (sic), 1997). 
Dennis Thompson (2007, p. 2) separates the broader concepts of individual and institutional cor- 
ruption, stating that institutional corruption is not just the "stark land of bribery, extortion, and 
simple personal gain," but also "the shadowy world of implicit understandings, ambiguous fa- 
vors, and political advantage." Thompson follows with, "identifying and assessing this kind of 
corruption depends critically on understanding the purposes of the institutions in which it takes 
place" (p. 2). Finally, deLeon & Green (2004, p. 72) move beyond the view of identifying the ac- 
tor to add the concept of pervasiveness of corruption; whether or not it is "systemic. " 

Narrowing the literature by the form, level, or size of corruption is fruitless, as corruption 
is crosscutting through political, institutional, business-level, and governmental red tape. Big 
Corruption, Grand Corruption, and espionage, or little corruption, scandal, barter, and petty thiev- 
ery; none of these accounts for the percent of GDP unaccounted for because of corruption, by any 
name. Dissecting corruption by form, level, or size is insufficient for this thesis, as the form of 
corruption does not necessarily inform the measurement required, nor does it inform the size of 
the overall corruption problem as it is relative to the GDP in a country. Broadening the literature, 

34 



however, to include the scope of corruption, considering the degree to which corruption is sys- 
temic is constructive toward understanding and measuring of its impact (deLeon & Green, 2004). 

One avenue scholars select to isolate the effects of corruption is to differentiate between 
that productivity that is reported and official, versus that which is not, regardless of scope, scale, 
color, and size. While this method offers more quantifiable data, it blurs the boundaries of the 
conventional definitions of corruption found in public affairs literature. Rent seeking and state 
capture are examples of the unofficial productivity. 

The seminal author on government privilege-seeking or protection-seeking is Gordon 
Tullock (1967), who suggests that tariffs (p. 225), regulation protection (p. 226) monopoly con- 
cessions (p. 228), transfers (such as favor by pressure from lobby groups) (pp. 228, 232), and 
barriers to entry (pp. 231) are forms of theft from the government's revenue stream. Anne O. 
Krueger (1974) penned the term rent seeking, and added as varieties, "bribery, corruption, smug- 
gling, and black markets" (p. 291), import restrictions (p. 298), quotas, license-allocation, fair 
trade and minimum wage policies (p. 301), credit rationing , and preferential tax treatment (p. 
302). Usage of the term 'rent' stems from Smith's (1776) account. "Wages, profit, and rent, are 
the three original sources of all revenue as well as of all exchangeable value. All other revenue is 
ultimately derived from some one or other of these" (p. 54). "All taxes and the [government] 
revenue which is founded upon them. . .are ultimately derived from some one or other of those 
three original sources of revenue. .." (p. 55). "Rent seeking," according to Tullock, is "the outlay 
of resources by individuals and organizations in the pursuit of rents created by government" 
(1993, p. 2). Consistent with Krueger (1974), Smith (1776), and Tullock (1967), asserts that rent 
seeking causes a "net waste of resources in inefficient production" (p, 228), it lowers government 
revenue, and diminishes official taxable individual and corporate income (p. 229). Krueger 
(1974) adds, "[diminishing returns will reduce the [labor] wage. The domestic price of imports, 
the distributive margin, and the [profit] wage of distributors will increase" (p. 297). 

35 



State capture is any group or social strata, external to the state, that exercises decisive in- 
fluence over state institutions and policies for its own interests against the public good" (Pesic, 
2007, p. 1). A form of grand corruption, it includes the ability of domestic or foreign informal in- 
stitutions or firms to mold or manipulate state laws, policies, or regulations (Kaufmann, 2003, p. 
21). At the highest level, officials setting public policy change or break rules to favor certain 
vendors, buy votes, or bargain for power (Chua, 2006). Essential to this thesis, Mauro, Abed, et 
al. (2002, p. 278) assert that extorting from education starts before the budget approval, so fewer 
dollars are allocated to education, and more are allocated to projects where that extortion is easier 
to hide. 

La Porta &Shleifer (2008, p. 7) concentrate their research on the difference between for- 
mal and informal firm productivity, using 14 African and 14 Latin American countries as the 
sample set, and OLS regression with data from informal surveys, educational attainment, and 
economic data. Raw materials, production costs, and electricity are the supply-side variables, 
while sales, GDP, and output are the demand-side variables. The individual is the unit of meas- 
ure, and the variables are normed to this. This method of approximating the size of the informal 
economy would have satisfied the needs and purpose of this thesis; however, as of yet, the data 
are not available for the sample set of countries in Eastern Europe. La Porta & Shleifer, (2008, p. 
7) "group the determinants of the size of the unofficial economy into three broad categories: the 
cost of becoming formal, the cost of staying formal, and the benefits of being formal." Many of 
the 'costs' of becoming and staying in the official economy are measureable, and belong with 
state capture and rent seeking, in the shadow economy. 

Similarly, to account for the corruption's effects on GDP, Schneider et al. (2010) use the 
term "Shadow Economy" (SE) to separate official and unofficial dealings. Shadow suggests that 
the unofficial activity is obscured or hidden, and better defines the productivity this thesis seeks 
to measure. "The shadow economy is an unobservable economic phenomenon, and no consensus 

36 



exists as to the definition of the shadow economy" (Buehn & Schneider, 2009, p. 5). Consistent 

with Dresher, Kotsogiannis et al. (2005), employing a multiple indicator, multiple causes 

(MIMIC) structural equation method. Since this is the only data set available that approximates 

the size of the informal economy that also covers the countries of interest herein, this thesis uses 

the Schneider data set, and the following definition for the shadow economy (Schneider et al., 

2010, p. 3). 

Shadow Economy Rules 

The shadow economy includes all market -based legal production of goods and 
services that are deliberately concealed from public authorities for any of the fol- 
lowing reasons: 

(1) to avoid payment of income, value added or other taxes, 

(2) to avoid payment of social security contributions, 

(3) to avoid having to meet certain legal labor market standards, such as mini- 
mum wages, maximum working hours, safety standards, etc., and 

(4) to avoid complying with certain administrative procedures, such as complet- 
ing statistical questionnaires or other administrative forms. 

Assuming that either productivity is reported or it is not reported is naive, however, add- 
ing that which we know to be reported with that which we can estimate to be unreported is a 
closer, though still problematic, approximation of the total GDP per country (Galbraith & Kum, 
2005; Schneider et al., 2010a). 
Causes and Consequences of Corruption 

The state's institutional environment is cooperatively managed through the highest-level 
authority of its governance processes. Governance performance can be measured by its ability to 
control corruption, which is particularly threatening to both aggregate and individual prosperity 
(Mauro et al., 2002). "Corruption is likely to be a symptom of wider institutional failures. . ." 
(Kaufmann, 2006, p. 98), and may hinder the accumulation of knowledge and technical capital 
and economic growth (La Porta & Shleifer, 2008). 

An overview of corruption around the world shows that many of its most commonly cited 
causes and consequences are thought to be economic in nature (Tanzi, 1998, p. 587). Mauro cites 

37 



causes related to rent-seeking through subsidies, price controls, and trade arbitrage, influence re- 
lated to trade restrictions or protectionist tariffs, and incentives or bribery stemming from low 
wages of civil servants, and other societal factors such as ethnolinguistic fractionalization and 
family ties (Mauro, 2000, pp. 4-6). Informal economic activity that operates outside of the formal 
economy has many pseudonyms including, but not limits to, three arenas, which may overlap. 
Recall that corruption's equilibrium necessarily allows benefit or gain for a portion of the actors. 
(1) The first is exchange of products and services (e.g., unofficial, underground, unobserved, un- 
reported, undeclared, non-transparent, informal, hidden, shadow, illegitimate, barter, cash, 
parallel, secondary, black, and gray economies or markets, of transactions that are on the ground 
versus on the books) (Feige & Urban, 2008; Schneider et al., 2010a, p. 3); Eurostat uses NOE, 
non-observed economy (Eurostat2011c). (2) Second is the trading for some sort of favor (e.g., 
lobby and special interest groups, Political Action Committee[PACs], labor unions, mafias, car- 
tels) (North, 1990). This includes "State capture," which is the ability of domestic or foreign 
informal institutions or firms to mold or manipulate state laws, policies, or regulations) (Kauf- 
mann, 2003, p. 21). (3) Thirdly, some gain by (or through) the trading of knowledge (e.g., 
espionage, trade secrets, copyright infringement, scientific breakthrough, or reason). These mar- 
kets fall into the broader sphere of corruption of the formal governance system (deLeon, 1993; 
Mauro, 2004b; Rose-Ackerman, 1999b; Schneider, 2009). 

Werlin (1994, p. 554) states that corruption. . ."arises out of the inadequacy of political 
software (persuasive power), particularly the distrust of governmental institutions" and, ". . .has a 
corrosive effect on the requirements for development" (2000, p. 182). Weak, formal, legitimate 
systems, ineffective political hardware (contracts, procedures) and influential institutions are 
symptoms of poor state management, or ineffective governance. Quoting Joseph Nye (1967), 
corruption seeps into the social, governmental, and political realms - it flourishes with "the weak- 
ness of social and governmental enforcement mechanisms; and the absence of a strong sense of 

38 



national community. . .[and the] weakness of the legitimacy of governmental institutions" (p. 

418). Regarding "the type of change which seems to be occurring in our age ("modernization") 

. . . [and to] the capacity of political structures and processes to cope with societal change," Nye 

states: 

[Modernization in the United States, or its] development (or decay) will mean 
growth (or decline) in the capacity of a society's governmental structures and 
processes to maintain their legitimacy over time (i.e., presumably in the face of 
social change). This allows us to see development as a moving equilibrium and 
avoid some of the limitations of equating development and modernization. 

As for consequences of corruption in the public sector, businesspersons see bribes as a 
form of tax, which increases prices. In Corruption and the Composition of Government Expendi- 
ture, Mauro finds ". . .evidence of a negative, significant, and robust relationship between 
corruption and government expenditure on education, which is a reason for concern, since previ- 
ous literature has shown that educational attainment is an important determinant of economic 
growth" (1998b, p. 277). Some forms of corruption have terminal consequences. "Under totali- 
tarian regimes, corruption is often directly linked to human rights violations," asserts Pope in 
Transparency International's (TI) Confronting Corruption (2000b, p. ix). 

Solving the causes of corruption question is vast beyond the scope of this thesis, and the 
consequences are far reaching, equally far outside its scope. Merton (1968, p. 130) asserts that 
the 'demand for services of special privileges are built into the structure of society." As deLeon 
reminds readers, "It is sown in Corruption" (quoting 1 Corinthians 15:42, 1993, p. 3). As such, 
"where it is rampant, there is no hope of controlling petty corruption" (Moody-Stuart, (1996, p. 
19). It is the corruption that avoids the democratic process that we seek to mute (Thompson, 
2007). 

The important point upon which scholars agree is that a layer of activity runs parallel to 
the formal system of government, and it attempts to avoid detection. The corruption that is meas- 
urably absent from the National Income Accounting revenue steam, and therefore, is missing 

39 



from official GDP reports, is corruption as a dimension of governance. In this thesis, this layer is 
called governance corruption. 
Corruption 's Remuneration 

Corruption's remuneration is "some type of significant personal gain, in which the cur- 
rency could be economic, social, political, or ideological" (deLeon, 1993, p. 25). Its value could 
lie in "the shadowy world of implicit understandings, ambiguous favors, and political advantage" 
(Thompson, 2007, p. 2). "We should not expect to find a sharp distinction between corruption 
and non-corrupt actions. Instead, we will find the gradations of judgment, reflecting a variety of 
equivocations, mitigating circumstances, and attributed motives" (Johnston, 1986, p. 379). Cor- 
ruption may be the act of an individual, or of a group or institution, to gain power, prestige, or 
position. The IMF and Good Governance (201 Id, p. 1) factsheet states, ". . .corruption thrives in 
the presence of excessive government regulation and intervention in the economy; substantial ex- 
change and trade restrictions; complex tax laws. ..tax incentives, zoning laws. . .and monopoly 
rights over exports and imports. . .(and poor) remuneration of the civil service." Power is a poten- 
tial pay-off for corruption (Nye, 1967, p. 421). Institutions gain respect because of the power 
they wield (Kooiman & Jentoft, 2009, p. 883; Mauro, 2004b; Schneider & Enste, 2002). "Power 
is inherent in governance" and shapes the capacity of governments and institutions to govern 
(Kooiman & Jentoft, 2009, p. 833). Good governance fosters the power of the state, while bad 
governance allows corruption to flourish, and nurtures informal activity. Some scholars assert 
that certain environments invite corruption, such as where monopoly power is in the hands of of- 
ficials, when the risks of getting caught are low and penalties are mild (Klitgaard, 1988; Rose- 
Ackerman, 1978). Other scholars assert that corruption can be predicted by "patterns of potential 
inducements or sanctions. . .[and the] structure of opportunities and incentives" (Sandholtz & 
Koetzle, 2000, p. 36). 



40 



Since corruption's remuneration is "some type of significant personal gain, in which the 
currency could be economic, social, political, or ideological" (deLeon, 1993, p. 25), it is im- 
portant to understand how scholars measure corruption in order to measure its impact on econom- 
ic growth, living standards, and income inequality. 
Controlling Corruption 

Interestingly, correcting the imbalances that cause corruption must be as multidimension- 
al as the corruption itself. "Any realistic strategy must start with an explicit recognition that there 
are those who demand acts of corruption on the part of public sector employees and there are 
public employees willing for a price to perform these acts. There is thus both a demand for and 
supply of corruption" (Tanzi, 1998, p. 587), and responsibility for acting illegally or unethically 
resides on both sides of corruption's equilibrium, which calls for horizontal accountability 
(Kaufmann, 2006). Mauro states, "[s]ince much of public corruption can be traced to government 
intervention in the economy, policies aimed at liberalization, stabilization, deregulation, and pri- 
vatization can sharply reduce the opportunities for rent-seeking behavior and corruption" (2000, 
p. 6). Rose-Ackerman suggests several successful anti -corruption projects as models for over- 
coming corruption of varying types, and case studies substantiate her theory that clean-running 
and stable governments are a key precursor to equitable, effective, and efficient economic growth 
that target both the supply and demand (Klitgaard, 1988; Rose-Ackerman, 1999b; Shleifer & 
Vishny, 1993). Policies aimed at curbing incentives for corrupt activity may be simple; increas- 
ing the penalty when caught, and/or increasing the law enforcement to catch it (Boswell & Rose- 
Ackerman, 1996), for example. While Thompson (2007) agrees on the mechanisms and that dis- 
covery should take place within the institutional form rather than in a criminal court, he asserts 
that the institutional complexity of governments must undergo structural reform in order to re- 
duce corruption. 



41 



Increasing the risk of exposure may help, especially as in political corruption. Transpar- 
ency International (TI) created the national integrity system to make undertaking corruption a 
"high risk and low return" endeavor (TI, 2000b, p. ix). TI suggests that the best method to "con- 
trol the cancer of corruption" is to increase both enforcement and prevention by using the national 
integrity system, which is "a system of checks and balances designed to disperse power and limit 
situations conducive to corrupt behavior" (2000b, p. xiii). Policies that affirm Freedom of Press, 
Freedom of Information, civil liberty protection and election oversight are critical when building 
a transparent governance (2000b, p. xxv). Transparency is critical in the private sector to reduce 
"capture" and increase foreign investment, calling for policies to further accountability in corpo- 
rate ethics and traditional legal and judicial reforms that focus on timely information, auditing, 
insider rules, and financial disclosure (Kaufmann, 2003, p. 21). 

"Corruption levels are determined by the overall level of the benefit available" (Boswell 
& Rose-Ackerman, 1996, pp. 84, emphasis in original). Decreasing corruption's available benefit 
is multidimensional, a task the World Bank took on in its "Multi-pronged strategies for Combat 
Corruption: Addressing State Capture and Administrative Corruption" (2000a, p. 39). The World 
Bank's framework starts with five major areas of focus listed next. Where novel approaches be- 
yond those mentioned above exist, those are listed, as well. (1) Institutional Restraints: 
independent prosecution and enforcement. (2) Political Accountability: Transparency in party 
financing, asset declaration, and conflict of interest rules. (3) Civil Society Participation: public 
hearings in the drafting of laws. (4) Competitive Private Sector: Monopoly regulation. (5) Public 
Sector Management: Merit based pay for civil service, customs oversight (ch. 4). 

Another vein of literature discusses increasing vertical and horizontal accountability by 
both state and non-state actors (UNDP, 2010e). Peer-level whistle -blowing protection and stake- 
holder groups are examples of horizontal accountability. From state actors, access-to-information 
laws are an example of vertical accountability (Reify, 2011). 

42 



Consequences of Corruption on Development 

Mauro (2000, pp. 4-6) following Nye (1967, pp. 421-423) cites several consequences of 
corruption. Corruption "lowers investment and retards economic growth to a significant extent, 
talent may be misallocated, [it] may reduce the effectiveness of aid flows, bring about loss of tax 
revenue." Pertinent to this thesis, Mauro (2000, p. 7) asserts, "[corruption may distort the com- 
position of government expenditure. ... [b]y reducing tax collection or raising the level of public 
expenditure, corruption may lead to adverse budgetary consequences, [and] lead to lower quality 
of infrastructure and public services." 

Citing empirical evidence from the former Soviet Union, Adeb & Gupta found that the 
". . .disintegration of the command structures in the old regimes triggered some of the most chaot- 
ic economic, political, and social changes in modern history. Absence of the rule of law and 
accountable systems of governance led to rent-seeking, corruption, and outright thievery" (Abed 
& Gupta, 2002b, p. 2). Kargbo (2006) writes of corruption that has hurt both aggregate (official 
production) and individual income in Africa. "Corruption leads to the decline in real per capita 
incomes, inflation, a widening budget and balance of payment deficits, and declining official pro- 
duction and exports" (p. 8). He reports, "[corruption has also led to massive neglect of the social 
sector, which has substantially decreased the quality of human resources in African states over 
the years" (p. 8). Specific to this thesis, "[t]he provision of educational and health opportunities 
have been limited, thus impacting negatively on the quality of life, labour, productivity, incomes, 
innovativeness, competitiveness, and poverty reduction in Africa States" (p. 8). The IMF Fact- 
sheet adds to the body of evidence on the relationship between corruption and education funding 
(IMF, 20 lid, p. 1). 

Corruption can reduce investment and economic growth. It diverts public re- 
sources to private gains, and away from needed public spending on education and 
health. By reducing tax revenue, corruption can complicate macroeconomic 
management, and since it tends to do so in a regressive way, it can accentuate in- 
come inequality. 

43 



Transparency International (TI) (2009b) and Mauro (2000) discuss corruption within the 
public education budget's system of allocation. "Countries with high levels of corruption invest 
less in public services, leaving the education sector under-funded" (p. 6). UNESCO reports that 
"between 10 and 87 percent of non-wage spending on primary education is lost" to "resource 
leakages" in the execution of the budget (Hallak & Poisson, 2007, p. 105). Mauro (1998, p. 277) 
presents "evidence of a negative, significant, and robust relationship between corruption and gov- 
ernment expenditure on education." Finally, Mauro (2000, p. 6) states the following about 
incentive to divert funds away from education in the budgeting and allocation process: 

Corruption may distort the composition of government expenditure. Corruption 
may tempt government officials to choose government expenditures less on the 
basis of public welfare than on the opportunity they provide for extorting 
bribes. ... education turns out to be the only component of public spending that 
remains significantly associated with corruption when the level of per capita in- 
come in 1980 is used as an additional explanatory [control] variable. 

Measuring Corruption - Indirectly 

Vito Tanzi (1998, p. 577) asserts, "[w]hile there are no direct ways of measuring corrup- 
tion, there are several indirect ways of getting information about its prevalence in a country or in 
an institution," including published reports such as newspapers, case-studies, empirical country- 
level data, and questionnaire based surveys (emphasis in original). The most prevalent method is 
the survey method. The World Bank and European Bank for Reconstruction and Development 
(EBRD) created the Business Environment and Enterprise Performance Survey (BEEPS) in 1999, 
and it is in its fourth iteration. The 2008 round conducted over 1 1 ,000 surveys of business own- 
ers and top managers on business climate and corruption. Data are available on 26 countries in 
Eastern Europe. While a strength of the BEEPS is in survey data specific to corruption, it is lim- 
ited to surveys of firms and employees of those firms about corruption in the business 
environment (BEEPS, 2008a). Therefore, it is not sufficient in scope for this thesis. The EBRD 
has published thematic Transition Reports yearly since 1994. In 1998, the theme was "Ten Years 
of Transition" (EBRD, 2010i), and the report included results from several surveys, and macroe- 

44 



conomic data for the transition counties backdated to 1989. With the backdated data, the EBRD 
dataset for is a sufficient source for eight of the seventeen variables on twenty-three of the thirty 
countries on which this thesis focuses. The variables that are not part of the EBRD dataset in- 
clude those in the Human Development Index and the component indices, Education Attainment 
Index, Life Expectancy Index, and Gross Domestic Product Index, which together make up eight 
variables, this poses no problem, as the HDI variables are each available from the HDR for each 
country. However, the two variables on which rest the key hypothesis in this thesis are not part of 
this data set: (1) Government Expenditures on Education as a percentage of Total Government 
Spending, (2) a measure for unofficial GDP per capita as a percentage of total GDP per capita. 
Since testing both of these variables requires macroeconomic data consistent and normalized with 
that of the World Bank and IMF, the EBRD data cannot be used in this thesis. EBRD in Transi- 
tion Report 2010 concludes the following regarding its own measure for progress. 

One problem is the subjective nature of the scoring and possible non -transparency of the 
demarcation between categories. ... This is because [the data] cannot be easily validated external- 
ly and creates a risk that a country's overall economic performance might influence the judgment 
about (and scorning for) its transition progress (which, in the extreme, would render regressions 
of growth on the transition indicators meaningless) (p. 2). 

The EBRD continues describing its fundamental concern with measuring economic pro- 
gress using this (or presumably other, similar) datasets. 

A more fundamental objection is that, with the exception of the infrastructure indicators, 
many of the scores reflect a rather simplistic view that a successful transition is mainly about re- 
moving the role of the state and encouraging private ownership and market forces wherever 
possible. The problem with this view is that markets cannot function properly unless there are 
well-run effective public institutions in place, (p. 2) 



45 



Several widely used surveys on the perceptions of corruption in the private sector and at 
different levels of government are available. Transparency International's (TI) Corruptions Per- 
ceptions Index (CPI) is one such survey. "The 2010 CPI measures the degree to which public 
sector corruption is perceived to exist in 178 countries around the world. It scores countries on a 
scale from 10 (very clean) to (highly corrupt)." However, the article continues, "[g]iven its 
methodology, the CPI is not a tool that is suitable for trend analysis or for monitoring changes in 
the perceived levels of corruption over time for all countries" (CPI, 2010b, p. 5). TI's Global 
Corruption Barometer uses similar methodology (GCB, 2009), and is a wealth of information, by 
country, on the scale, type, causes, consequences and true accounts of corruption, but offers no 
measurement of it relative to GDP (GDR, 2009c), as that is not its purpose. 

The Cost / Benefit Analysis (CBA) allows one to value the implicit and explicit costs of 
corruption using empirical and survey data (Sen, 2000). Kaufmann (2006, p. 81) suggests that, 
"Corruption can, and is being, measured in three broad ways: (by)... gathering the informed 
views of relevant stakeholders. ..tracking countries' institutional features (and by). ..careful audits 
of specific projects." Together, the CBA and national income accounting tools can estimate the 
portion of funds that are unavailable domestically due to corruption by budget line item and do so 
with statistically significant precision. This precision increases, and costs become increasingly 
explicit as more nations practice standardized accounting methods (IMF, 2011, p. 1; D. K. Gupta, 
2001; Weimer & Vining, 2004). However, National Income accounting practices are yet incon- 
sistent between countries and links between social and professional research are lacking, 
requiring us to abandon the CBA method for this thesis (Feige & Urban, 2008). For an example 
of the chasm rendering the CBA unproductive for the method herein, deLeon (1998) attempts to 
"chart a middle course" between the positivists and post-positivists representing respectively the 
purely quantitative and purely qualitative method extremes (p. 1 1 1). To elucidate the division, 
deLeon affirms the post-positivists argument "that too often the policy analyst is well removed 

46 



from the political and value conflicts inherent in public policy" thereby making difficult the bene- 
fit assessment. However, the positivists "play an important role in policy analysis, for it is by us- 
using many of these quantitative techniques that they can propose with some rigor a clear-cut set 
of expectations necessary for prediction purposes" (p. 110). Further, in Weimer's (Weimer, 
1998, p. 118) words, "one would be hard-pressed to find any important public policy decisions 
that were made solely on the basis of cost-benefit analysis" (quoted in deLeon, 1998, p. 108). 

Several scholars critique the MIMIC method used by Schneider et al. (2010) asserting 
that the variables used in the MIMIC equation are "highly correlated with each other" (Breusch, 
2005; La Porta & Shleifer, 2008, p. 8), possibly exaggerating the estimated size of the informal 
economy, and that the benchmarks are subjective. Recall also, that correlation coefficient be- 
tween the electricity demand method, currency demand, key perceptions indices and structural 
equation methods, is .88 or greater (S. Gupta et al., 1998, p. 12), so the concern runs across the 
potential methods. Addressing the subjectivity of the benchmarks, Schneider and other scholars 
collaborate with Bruesch to arrive at a calibrated benchmark that holds a "proportional relation- 
ship between the measurement in different years" (Dell'Anno & Schneider, 2006, p. 9), avoiding 
overstating the percentage of the Shadow Economy. Schneider et al. (2010a) counter with the 
following statement, regarding the MIMIC (Multiple Indicators Multiple Causes) model. 

[T]his is the first study that applies the same estimation technique and almost the same 
data sample to such a large number of shadow economies. . .[using] the MIMIC estimation meth- 
od for all countries, thus creating a unique data set that allows us to compare shadow economy 
data. (p. 3) 

According to the IMF scholars on corruption estimation, ". . .little is known about the de- 
velopment and the size of the Shadow Economies in developing Eastern European and Central 
Asian countries. . . in the recent past" (201 Id, p. 1). The criticism by Bruesch relative to this the- 
sis is the suggestion that the MIMIC model may overstate the actual Shadow Economy 

47 



percentage. However, other scholars assert that corruption accounts for far more of the economic 
activity than studies to date have realized or uncovered (Levy, 2007). Since the size and scope of 
the MIMIC technique and study of national -level government and institutions is revolutionary 
and methodologically unmatched, it therefore, is the measure employed for the methodology in 
this thesis (See MIMIC diagram in Appendix). 

Theoretically, the relationship between corruption and the Shadow Economy is thus un- 
settled. There is, however, reason to believe that the relationship might differ among high and 
low income countries (F. G. Schneider, 2009). Others assert that the relationship is complex, de- 
pending on the maturity of the government, and the quality of governance (La Porta & Shleifer, 
2008). Recent literature asserts that corruption and the Shadow Economy are substitutes when 
corruption is high, and complements when corruption is relatively lower, supporting the need to 
measure them separately in a simultaneous equation model (Buehn & Schneider, 2009; Dreher, 
Kotsogiannis et al., 2005; Russell, 2010). 



48 



Economic Growth 

The business cycle literature is foundational for understanding the effects of corruption 
for two reasons. First, much of the business cycle literature precedes the economic growth litera- 
ture chronologically, and more importantly, it is referenced frequently in the latter. Second, this 
thesis pivots on the political lightning bolt - the end of the Cold War. Baumgartner & Jones 
(1993) call this non-incremental change a 'punctuation' in society's equilibrium or stasis (p. 23). 
A watershed event, this demarcation was a catalyst for a new business cycle and economic 
growth stage for many Central and Eastern Europe countries (Rostow, 1991). The economic 
growth literature underscores the criticality of education funding in economic policy development 
and of this thesis by underscoring required to build strong, healthy, and viable economic devel- 
opment today, with tomorrow's staying power. In Innovation: The New Pump of Growth, Paul 
Romer asserts that it is the application of knowledge through an educated workforce such as high- 
ly trained scientists and engineers who are to credit for past economic growth, and that growth 
has proven to be unsustainable in countries around the world without sufficient public support 
through funding and public policy (Romer, 1998b). Developing this argument begins with an un- 
derstanding of the business cycle. 
The Business Cycle - the foundation of economic growth stages 

Business cycle literature falls into two major categories, the how cycles work — their 
identification and measurement, and, the theories on why — or their cause. From here, the litera- 
ture divides again into endogenous non-linear and exogenous linear-multiplier, with some helpful 
bridges between them. This pattern repeats itself in the economic growth literature. Economic 
growth's reputation is on the line, as some claim it is economic growth itself that can exacerbate 
poverty or income inequality {e.g., Galbraith, 2008; Rothschild, 1986; Pritchett, 1997), while oth- 
ers claim economic growth is required to alleviate both (e.g., Sachs & Warner, 1995; Sen, 1999; 
Barro 2001; Friedman, 1997). Either way, aggregate growth seems inevitable (Maddison, 2009); 

49 



furthermore, growth is merely a measure of the change in annual official GDP output, so culpa- 
bility or credit belong to the causes of change. Complicating the equation, both sides may still be 
right, if given identical data and definitions, the time span pivots on different events or cycles 
(Galbraith & Kum, 2005; Pritchett, 1997; Rostow, 1991). 

Early work on business cycles focused on identification and measurement and questioned 
whether movement equals a cycle or an aberration. What magnitude of change constitutes a sta- 
tistical oscillation rather than a cycle? Is it repeated or at least periodic, continuous, intermittent, 
or patterned (Mitchell, 1928; Slutsky, 1929)? Regarding the naming of cycle names their dura- 
tions, Juglar (1893), working on the credit crisis in France and later in the US, identified 8 to 10 
year industrial cycles tied to the issuance of credit. Kitchin is credited for identifying the 3.5-year 
business cycle (1923, p. 10). Kitchin, Slutsky (1929), and Wright (1920), considered the short 
cycle's normal oscillations due to human psychology. Kondratiev identified half-century "long- 
wave" cycles (48 to 60 years) that incorporate several shorter waves (Kondratiev, 1926). Schum- 
peter solidified these three time-spans by the author's names — Kitchin, Juglar, and Kondratieiv 
(1926) — into the economic growth literature of today with his theory that each represents an in- 
novation of different magnitude, exists simultaneously, and should be additive with revolutionary 
change creating a higher "steady state" (Schumpeter, 1939). Mitchell, in turn, tied a 4-year cycle 
to the effects of the political cycle and found two or three of these shorter cycles exist between 
Juglar crises (Glasner & Cooley, 1997, p. 347). 

In a foundational work to the developing economic growth and governance theories, 
Rostow adds that the economic stages of development are logical, rational, and based on exoge- 
nous globalization and endogenous governance forces. Rostow (1991) found the pre- 
conditioning stages were roughly 15 to 20 years and the take-off stages were about 60 years, con- 
sisting of several interwoven growth spurts of varying lengths and magnitudes depending on the 
industries involved and the "spreading" effects of lateral interaction between industries. Kuznets 

50 



(1940) proposed a 15 - 20 year cycle consistent with the explanation of interim shorter cycles 
within larger growth phases and based on his own national income research. Burns & Mitchell 
(1946) developed a definition for the business cycle, which is the duration in months from trough 
to trough when measuring the rate of change of productivity; this set the standard for other coun- 
tries. Note the identification of a standard: a trough marks a cycle. 

After working out the statistical problems with the counter -cyclical (and therefore, the 
canceling-out) nature of supply and demand forces within aggregate indexes, from these diver- 
gent camps based on theory, come a general agreement that economies do cycle. Given that 
economies cycle, one may ask how the economy cycles, referring to the initial date, degree, and 
duration of the cycle. This elevates the pertinent question to why - why do economies cycle? 

Following Keynes' (1936) lead on why economies cycle and why they grow, many econ- 
omists modeled endogenous causes with some type of oscillator such as income/expenditure 
(Samuelson, 1939), income/savings (Kaldor, 1959), inventory (Metzler, 1941), trade (Hicks, 
1950), and credit and money supply (Hayek, 1933). Some scholars added non-linear or "dynamic 
effects" (Goodwin, 1951), price signal, or information on intertemporal discoordination (Hayek, 
1933), or time lags between acquisition and distribution (Kalecki, 1954), or the idea of a multipli- 
er (Howitt et al., 1999). Other scholars, following Schumpeter and Kuznets, worked on 
exogenous causes such as structural change and entrepreneurial gains (Schumpeter, 1942), 
knowledge accumulation (Romer, 1996), shocks with a "ratchet effect" (Smithies, 1957), and 
technical change (Hicks, 1950; Solow, 1956). 

Summarizing the review of business cycle literature - economies cycle. Cycles are the 
effect; cycles do not materialize out of a void; they have a cause. This critical point refers to 
Rostow's economic growth stages: events demarcate and catalyze economic growth stages 
(Pritchett, 1997; Rostow, 1975, 1991; Xu & Li, 2008). The cause(s) is (are) due, generally, to 
some influence or to a combination of endogenous, evolutionary change and exogenous revolu- 

51 



tionary shifts in the steady state (Ofer, 1987; Schumpeter, 1942). Like the business cycle itself, 
"The Kuznets curve.. .emerges as a clear empirical regularity...." (Barro, 2000, p. 32). The esti- 
mated relationship may reflect not just the influence of the level of per capita GDP but also the 
dynamic effect; whereby, the adoption of each type of new technology has a Kuznets-type dy- 
namic effect on the distribution of income (Barro, 2000). Business cycle research was a 
foundation for an explosion of attention on economic growth, which offers theories and models 
on why and how economies grow. 
Economic Growth Theories 

A country's economic growth, defined as a long-term change in capacity to supply in- 
creasingly diverse economic goods to its population (Kuznets, 1973, p. 247), is based on 
advancing technology and the institutional and ideological adjustments that it demands. Econom- 
ic growth, generally, is the change in aggregate producing power, GDP, which is the amount of 
goods and services produced in an economy over one year. A negative change is negative 
growth. In addition, the "realized" growth rate is the total growth rate minus inflation. Kuznets 
(1940, pp. 259-259) elaborates on Schumpeter (1939), who distinguishes economic growth from 
economic development by the degree of change, where growth is incremental, evolutionary, and 
continuous and where development is characterized by "discontinuity of the steady-state. ..a dis- 
ruption of the static equilibrium leading to an indeterminate future equilibrium" (Kuznets, 1940, 
p. 259). A consequence of evolving societies, increasing population, production of basic necessi- 
ties, and reliance on incentive to spur on economic activity (Phelps, 2008; Schumpeter, 1939, 
1942) both fuel and are fueled by inflation; what Rostow (1991) called the "compound interest" 
of an economy (pp. 4-6). The staying power of a new steady state requires constant progress in 
the pre-conditions to it, in infrastructure and social capital (Putnam, 2000). Staying power also 
requires the foresight to invest in those factors that will be valuable in the future both for con- 



52 



sumption and demand for exports (Krugman, 2000; Rostow, 1991). It is critical to assessing the 
quality of the economic development ex post facto. 

After the development has occurred, analysts can see the evidence of healthy or un- 
healthy economic growth by an economy's ability to meet the needs of its citizens, and its 
customers. Citizens produce the inputs to GDP, the goods and services produced in a country in a 
year. In addition, citizens (and the additional world's population) are consumers, or customers, of 
that which is produced; if the product is desirable and the price is commensurate with its per- 
ceived value. For example, assume Country A and Country B both decided to increase GDP by 
pursuing additional shares of the world transportation market: Country A pursues the horse 
drawn carriage, and Country B, racecars. Even the finest horse-drawn carriage has a limited ap- 
peal in the local or world market, yet the price is relatively low. Likewise, even the latest, high 
priced and technologically advanced racecars have limited appeal, yet the horsepower is high. 
Both are in the realm of transportation, however, neither is meeting a high demand in the popula- 
tion. Neither provides the host country a sustainable industry nor creates a trading advantage. As 
indicated above, there must be a desirable product (with high demand) and a commensurate price 
and value in the international market to gain economic development strength. Neither the horse- 
drawn carriage nor racecar ideas would meet the current or future needs of the citizens or of the 
export markets. The sustainable economic development depends on the public officials' ability to 
forecast, based on foresight, the investment needs in infrastructure, education, training, research 
and development, and expertise to produce needed and desirable goods for the times; and to inno- 
vate and revolutionize to meet the challenges for tomorrow and for the world market (Nelson & 
Phelps, 1966, pp. 70-71). 

In review essay of economic growth literature, Klenow & Rodriguez-Clare (1997) chal- 
lenge researchers to complete four steps when assessing economic growth: (1) "more tightly link 
theory and evidence... (2) tie research to business cycles... (3) develop more theories of interna- 

53 



tional productivity differences... (4) and, collect detailed country data bearing on the process of 
technology diffusion" (Barro, 2001a; Klenow & Rodriguez-Clare, 1997, p. 597; Klingner & 
Sabet, 2005). The rationale for this thesis parallels Klenlow & Rodriquez-Clare's four-step chal- 
lenge. First, the thesis accomplishes steps one and two by linking governance, economic growth 
theories, and individual income with stages of economic growth. Second, this thesis satisfies 
steps three and four by analyzing individual income as a measure of education value based on the 
staying power of economic growth in a sample of countries. New Growth Theory (NGT) is a 
theoretic umbrella over the four challenges above (Barro, 2001b; Klenow & Rodriguez-Clare, 
1997). Stated differently, this thesis satisfies the challenges, capitalizing on NGT's treatment of 
education as fundamental to successful and sustainable development policy (Romer, 1998a). 
Theories on the Causes and Types of Economic Growth 

Two theoretical camps divide the economic growth literature based on causes (why), then 
further divide based on effects (how). The first camp asserts that exogenous forces cause eco- 
nomic growth, the second camp that endogenous change cause economic growth. The literature 
further divides, based on the effect of the growth; the two camps divide into four based on how 
economies grow. This latter division is centered on the path of growth -the "trajectory"; either 
individual incomes converge (the gap between the richest and poorest shrinks) or diverge (the gap 
between the richest and the poorest grows), or move in some combination of these paths over 
time. Each of these four major theoretical camps, two on causes (exogenous and endogenous) 
and two on effects (convergence and divergence), inform this thesis' study on the causes and ef- 
fects, the why and how, of economic growth. This large literature is important to understanding 
the nature of economic development. 
Neoclassical Growth Theory and Exogenous Growth Theory 

Solow isolated key determinants of economic growth into the factors of production, tech- 
nology, labor, and capital, isolating the growth attributed to each. His work laid the foundation 

54 



for Neo-Classical Growth Theory. From the literature on technical change grew the burgeoning 
literature on the rate of technology and innovation transfer, adoption, and diffusion as a measure 
of policy economic development and stability (Klingner & Sabet, 2005). Solow wrote, regarding 
the forty years ending in 1949, "[g]ross output per man hour doubled over the interval, with 87 Vi 
per cent of the increase attributable to technical change and the remaining 12 Vi per cent to in- 
creased use of capital" (1957, p. 320). 

Consider the tremendous change and growth in the transportation industry, for instance, 
with the aid of automation. Fredrick Taylor (1911, p. 21) predicted this rapid change when ap- 
plying the principles of scientific management, which include the "scientific education" and 
scientific skill training of workers toward the needs of the future, or to infuse the workplace with 
a "scientific knowledge" that promotes ingenuity. In scientific knowledge, the worker "is quickly 
given the very best knowledge of his predecessors; and, provided. . .with standard implements and 
methods which represent the best knowledge of the world up to date, he is able to use his own 
originality and ingenuity to make real additions to the world's knowledge, instead of reinventing 
things which are old" (p. 126). Rostow referred to this shift in economic growth stages, the shift 
from the "pre-conditions to take off' stage, to "take-off (1991, p. 5). 

Rostow stated, "whenever these principles are correctly applied, results must follow 
which are truly astounding" (p. iii). Clearly, a doubling (or 100 percent change) of the average 
person's productivity or output per hour over forty years is astounding, yet it makes sense in light 
of the assembly line's coming of age during the same forty years. Solow (1957) attributed 87 Vi 
percent of that doubling to technical change, one such type of change being automation (p. 320). 
Henry Ford produced 1 1 Model T cars in the first month of its production in 1909. Then, Ford 
automated its assembly line, implementing Fredrick Taylor's theories on Scientific Management 
(1911a). In 1910, 12,000 Model T cars rolled off the assembly line, and by 1925, 2 million Mod- 
el Ts rolled off that line (Brinkley, 2003, p. 475). 

55 



Solow (1957) states that the remaining 12 V2 percent of the doubling of productivity per 
working hour over his forty-year study was due to the increased use of capital (p. 320). Ford built 
the Highland Park Ford Plant in 1913 to accommodate the Model Ts assembly line (Brinkley, 
2003), an example of increased use of capital. Taylor writes on, "the same principles can be ap- 
plied with equal force to all social activities: to the management of our homes; the management 
of our farms; the management of the business of our tradesmen, large and small; of our churches, 
our philanthropic institutions our universities, and our governmental departments" (191 la, p. 8). 

Relying on a Keynesian foundation to apply external stimuli to an otherwise closed sys- 
tem, Solow's findings revived Keynes' work leading some scholars to isolate specific factors, 
while others focused on production disincentives (Howitt, 1986; Keynes, 1936; North, 1994). 
Working from these strengths, Exogenous Growth Theory specifically employs global technology 
advancement as the exogenous agent of growth (Hahn & Solow, 1997; Howitt, 1986, 1997). Ei- 
ther way, models of exogenous theories treat growth as the result of some external catalyst, 
neglecting part of the evidence, such as cycles inside the economies and additive or compound 
growth. Returns on endogenous factors {e.g., interest, inflation, population or compound growth, 
incremental knowledge gains over time, or inventions that revolutionize productivity, etc. . .) 
mathematically eliminate the possibility of exogenous growth, arguing for a different growth mo- 
tive, a point conceded by many neoclassical theorists (Romer, 1996). 

Critics of exogenous growth theories contend that this camp forces illogical conclusions 
that neglect variation among countries in technical accumulation, and neglect the effects of hu- 
man capital generally, and knowledge capital, specifically. In doing so, this camp disregards vast 
literature on the effects of such variables as technology diffusion and adoption (Easterly & 
Levine, 2001; Klingner & Sabet, 2005; Romer, 1990; Taylor, 1911a), educational attainment and 
quality (UNESCO, 2010d; 2001b; Barro & Lee, 1996), knowledge spillovers (Arrow, 1962), 
governance (IMF, 201 Id; Abed & Gupta, 2002b; Kaufmann et al., 2008), and corruption 

56 



(deLeon, 1993; Rose-Ackerman, 1978; Tanzi, 1998). Therefore, a Keynesian-based theory is in- 
sufficient for this thesis. 
Endogenous Growth Theory 

Endogenous Growth Theory refutes Solow's work. Literature from the endogenous 
growth camp treats factors such as governance, policy, effects of the national economic and fi- 
nancial systems, education and innovation, social capital, and incentives as agents that develop 
human and social capital and drive incremental growth from within. These work in conjunction 
with technical progress and innovation (Barro et al., 1994; Barro & Sala-i-Martin, 2004; Putnam, 
2000; Romer, 1994b). While exogenous growth models require holding technical advancement 
constant across countries, endogenous models treat technical change as a variable (Easterly & 
Levine, 2001; Romer, 2001). Ironically, advances in computing technology since Solow's semi- 
nal work on exogenous technical change was its undoing. The ability to manage and calculate 
large cross-country longitudinal data sets allowed researchers to treat more variables as variables 
rather than constants (Cobb & Douglas, 1928; Sala-i-Martin, 1997). When applied to evidence in 
westernized, democratized countries, and/or to isolated and self-reliant regions (e.g., the US, 
UK), use of endogenous theory yield strong correlations between sound governance and growth. 
This becomes important when considering economic policy options in the US, and/or for non- 
westernized communities that rely on endogenous factors for growth (King & Levine, 1993; 
Martin & Sunley, 1998). 

However, this theory neglects a different part of the evidence: revolutionary shifts, tech- 
nology adoption rates, and other effects of external events (Easterly & Levine, 2001; Solow, 
1956). Endogenous growth neglects the volume of literature on the stages of economic growth. 
Rostow (1991, p. 6) cites that sometimes,"[T]he stage of preconditions arise not endogenously, 
but by some external intrusion by more advanced societies. These invasions-literal or figurative- 
shocked the traditional society and began or hastened its undoing." Therefore, both endogenous 

57 



and exogenous theories are guilty of remaining true to their precepts at the expense of evidence. 
Endogenous Growth Theory neglects external factors such as peace treaties or a neighboring 
county's research on solar energy, where Exogenous Growth Theory neglects internal factors 
such as education spillovers or positive externalities of a hometown Olympic athlete. 
New Growth Theory 

New Growth Theory links the roots of endogenous economic factors to technical progress 
adoption and diffusion increases that drive economic growth. New Growth Theory builds a 
bridge between endogenous and exogenous camps. For example, assume that a new technology 
in an era of rapid globalization developed outside the economy in question. Each economy must 
choose whether to employ its own resources in order to adopt the exogenous catalyst, or not. 
Romer (1998a, p. 2) describes the process thus. 

New Growth Theory identifies three specific features that make growth possible. First, 
we live in a physical world that is filled with vastly more unexplored possibilities than we can 
image, let alone explore. Second. Our ability to cooperate and trade with large numbers people 
makes it possible for millions of discoveries and small bits of knowledge to be shared. Third, and 
most important, markets create incentive for people to exert effort, make discoveries, and share 
information. 

Specifically, Romer's concept of knowledge as an economic asset (non-rivalrous, partial- 
ly excludable, human capital with increasing returns) is fundamental to potential growth. "If a 
poor nation invests in education and does not destroy the incentives for its citizens to acquire ide- 
as from the rest of the world," Romer states, "it can rapidly take advantage of the publicly 
available part of the worldwide stock of knowledge" (2007, p. 3). Romer asserts that living 
standards are a direct result of knowledge and technology adoption (Romer, 1993). Phelps (2008, 
p. 14) attributes a good economy to education, which produces vitality, and policies that promote 
inclusion. 

58 



New Growth Theory proponents defend good governance as a practical necessity for ed- 
ucation and the educated to flourish, drawing from classical economic theorists and new ideas 
from the governance literature (Phelps, 2008). North (1992, p. 3) suggests economists treat the 
policy development and implementation as a function of governance, ". . .as a critical factor in the 
performance of economies, as the source of the diverse performance of economies, and as the ex- 
planation for inefficient markets." The assertion that Social Capital is a pre-condition to a long- 
wave growth stage from the political economy literature, parallels New Growth Theory, entrepre- 
neurial theories, and theories on human capital (Matheson, 2008; Nelson & Phelps, 1966; Phelps, 
2008; Putnam, 2000; Rostow, 1991; Schumpeter, 1939; H. A. Simon, 1986; Xu & Li, 2008). 

Technology and innovation diffusion and adoption are both precursors and by-products 
of the quality and quantity of education, GDP, economic growth and economic development by 
country; the speed and degree of its transfer are, in part, a result of the governance system, and 
the success of its development policy implementation, specifically, of education policy (Nelson & 
Phelps, 1966; North, 1992; Romer, 1993). "Innovation diffusion and adoption describes the 
spread of new products, values, policies, or processes beyond the locus of their original success. 
If viewed purposively, this spread can be described as both organizational learning and 
knowledge management" (Sabet & Klingner, 1993). This concept of adoption closely resembles 
Taylor's principles of scientific knowledge, scientific learning, and scientific management 
(1911a). Klingner & Sabet (2005) summarize the importance: "the true measure of these innova- 
tions' value lies in the effectiveness of shared information and transferred knowledge to attain 
societal goals like sustainable development" (p. 206). 

Critics argue that education does not produce increasing returns. Instead, education is 
like other economic factors experiencing constant returns to the investment in education (Solow, 
1957), diminishing marginal utility and rent seeking (North, 1990), crowding out by other activi- 
ties such as leisure, and quality or effectiveness challenges (Pritchett, 2001, p. 369). Rather than 

59 



knowledge spillovers and positive externalities from education, put forth by New Growth Theory, 
recent studies provide evidence of negative education externalities and "lack of a correlation be- 
tween growth and education expansion. . .and schooling variables" (p. 380). Importantly, Pritchett 
(p. 382) asserts, "[r]ent seeking and directly unproductive activities can be privately remunerative 
but socially dysfunctional and reduce overall growth" which agrees with North (1990) suggesting 
that informal institutions may benefit from education. One key challenge to this argument is con- 
sistency in definitions. Returns to education (its funding cost/benefit balance) and returns to 
knowledge are different. 

For the evidences of and requirements for economic growth, this thesis builds on Romer 
(1996), and Easterly & Levine (2001), by modeling dimensions of governance, development pol- 
icy, education, and market demand of output. New Growth Theory stands apart from other 
economic growth theories for three reasons: it shoulders change regardless of origin, pace, or va- 
riety, it allows researchers to treat factors of growth as variables or constants, and it incorporates 
education as a variable in economic growth. 
Convergence, Divergence, and Bridging Theories 

Incomes convergence over time 

Robert Barro starts with Solow's neoclassical growth model for evidence of "automatic 
forces that lead to convergence over time in the levels of per capita income and product" (1991, 
p. 1). The homogeneity of US data earned credit for much of the statistical strength in his model. 
This point is significant (and expanded later) as an indicator of internal, freer market forces tend- 
ing to cause converging incomes (Barro, 2000). Convergence Theory maintains credibility for 
use in discrete situations, but until recently, had done little to inform economic globalization 
(Barro, 2000, 2001a; Olson, Sarna et al., 2000; Jeffrey D. Sachs, 2005; Sadik, 2008). Conver- 
gence Theory may explain why incomes in Kuznets' maturing economies grew increasingly 
similar. Clearly, according to Sachs & Warner (1995), it is the "policy choices" that underlie 

60 



each country's economic realities and have allowed countries into the '"convergence club'" or 
kept it trapped in poverty shy the human capital to raise it up (p. 4). 

Incomes divergence over time 

Divergence Theory suggests that incomes diverge over time in certain situations. It 
gained widespread recognition as the refutation to the convergence literature (Easterly & Levine, 
2001; Matheson, 2008). Substantial scholarly work and research on discrete datasets agree that 
there are likely correlations between economic growth and divergent incomes (Baddeley, 2006; 
Pritchett, 1997), including Rothschild's "recalculation of Kuznets' intersectoral inequality ratio 
for the private nonagricultural economy between 1948 and 1982 [which] results in a significant 
increase in sectoral inequality" (1986, p. 205). Stated otherwise, incomes between the richest and 
poorest in this particular data set grew apart. Divergence Theory may explain why incomes in 
Kuznets' immature economies grew increasingly disparate. Pritchett (1997, p. 15) suggests that 
the divergence is a matter of progressiveness or "backwardness" of the "fabric of civic society." 
He follows with a prescription for growth policy similar to Sachs & Warner (1995a, p. 10), that 
countries must provide an open economy, free of repressive regulations, with relatively low levels 
of corruption . 
Bridging Theories 

Finally, five theories offer a bridge between the convergence and divergence camps. 
Knowledge about the difference between camps is the critical issue for many policy analysts and 
public administrators and policy makers, as the outcome bears great weight on long-term individ- 
ual and aggregate prosperity (North, 1991b). First, poverty trap theories suggest simultaneous 
convergence and divergence. For instance, if policy makers neglect to appropriate adequate fund- 
ing for education, empirical evidence shows that the poorest sector of the population will grow 
relatively poorer while the richest grow relatively richer (Romer & Barro, 1990). 



61 



Second, Schumpeterian Growth Theory asserts that growth comes from quality- 
improving research and development innovations. This theory informs the balance of economic 
growth theories and adds validity to both camps, as each require innovation regardless of its 
origin (Howitt, 1999; Howitt & Mayer-Foulkes, 2005; Phelps, 2008). 

Third, in The Stages of Economic Growth, Rostow (1991) offers an historical view of 
characteristics of economies in different stages, which, in turn, offers potential bridges between 
the four major economic growth camps, allowing each to be right under certain conditions. For- 
tunately, this suggests a futility in damning converging or diverging theories, as these mean little 
without knowledge of the relative stage and trend of the economy, and less without an accounting 
of the variable's composition. It is insufficient to ask a question requesting results on income tra- 
jectories absent considerable context. 

Path dependency theories suggest that the economy's behavior may take time to adjust, as 
investment in land, labor, and capital, are somewhat directional (North, 1991b). The movement 
of individual income inequality may be the result of a combination of factors from legacy eco- 
nomic investments, which take time to adjust, and legacy skill sets, which take time to re-train. 

Lastly, Kuznets' data support both diverging and converging income inequality. He aptly 
applied different data scenarios to discover the determiners of inequality, while he admitted that 
the data available left much unknown, and part of the credit for the richness of information avail- 
able now goes to him for the challenge he presented. Kuznets (1934, pp. 6-7), as the innovator of 
National Income accounting, knew and reported the limitations of GDP per capita as a measure- 
ment for individual welfare or "the reverse side of income". 

In summary, the convergence / divergence argument remains a vital and productive field 
that tells policy makers and analysts nothing, or, worse, can be skewed to tell them anything, ab- 
sent considerable context. For this reason, and because the presence of corruption skews the very 
data policy makers need to make development decisions (Schneider et al., 2010), it is critical to 

62 



inform public administrators and their policy development endeavors that good governance and 
control over corruption is important (Tanzi, 1998). Public budgets (e.g., infrastructure, health 
care, public works, and courts) suffer, in the presence of corruption. This thesis defends that si- 
phoning funds away from education does more harm than siphoning funds from other publically 
funded programs as reduction in education budgets has an increasingly detrimental effect on eco- 
nomic growth when the others' effects are, at best, constant (health care) or decreasing 
(depreciating assets) (Mauro, 1997). Mauro asserts "corruption lowers expenditure on education" 
(p. 267). 

New Growth Theory is critical to developing the healthy growth policy arguments. Edu- 
cation funding as a part of development policy must be protected, as education, and consequently, 
society's ability to adopt and use technology effectively, hinges on this protection. This may ex- 
plain why healthier, more mature economies with better governance and control over corruption 
experience less poverty, less inequality, and converging incomes; however, this is a thought for 
future research. 



63 



Measuring Education Delivery 
Measuring the Quantity of Education (Supply) 

The body of literature on assessing and measuring education is enormous. The literature 
surrounding education delivery includes research on quality, quantity, and process measurements. 
Quality measures for education include the output-based test scores and literacy rates, and on in- 
puts such as materials, facilities, class size, and teacher training (UNESCO, 2005; D. J. Brewer, 
Krop et al., 1999, p. 187). Quantity measures for education include matriculation rates, years of 
schooling, level of school attainment, and gender parity (Barro & Lee, 1993, pp. 365-368). These 
factors are critical for assessing education systems, however, they measure education delivery 
based on an externally developed budget. This body of literature, which informs education as an 
industry, discipline, or process, is outside the scope of this project. Rather, the focus for this the- 
sis is public expenditures budgeted for public education, and the effects of corruption on the 
public expenditures for public education. Education funding provides its own body of literature. 
However, this thesis focuses on empirical data indicating the level of public funding from official 
budgets, or "supply-side" financing (Patrinos, 2007, p. 1). Therefore literature on other funding 
or school choice methods, to include private schools or private funding (M. Friedman, 1997), 
voucher programs and school choice (M. Schneider et al., 1997; Teske & Schneider, 2001), bus- 
sing, scholarships, grants, foreign aid, for-profit schools, and "demand-side" mechanism 
(Patrinos, 2007, p. 3) is excused for the purposes of this thesis. 
Measuring the Value of Education (demand) 

Another body of literature, equally vast, measures the value of the schooling provided to 
the individual (e.g., living standards, individual income, human development, and contribution to 
society) and to society (returns to education, national and international market value of goods and 
services). This work is quintessential to the questions in this thesis. Sen integrates these volumes 
of work into his work on Human Development (HDR, 2009e; Sen, 1997, 1999, 2004). He syn- 

64 



thesizes work by other Nobel Laureates into the human development literature by including edu- 
cation as a means to economic development (e.g., Kuznets' work on accounting for individual 
income in National Income accounting [1934, 1955]; Solow's work on technical change, [1957]; 
Arrow's work on learning by doing, [1962]; and Simon's ideas on bounded rationality, [1997]) 

Sen added work by noted scholars on the subject, as well (e.g., the work of Sachs & 
Warner, on knowledge spillovers and economic policy, [1995]; Romer's work on education and 
technical change [1990]; proceedings for the World Bank on development through education by 
Romer et al. [1992]; as well as Romer's work on endogenous change, investment in education, 
and New Growth Theory [1994, 1998]; and Barro's work in educational attainment and human 
capital [1993, 2001, 2001b]). Together with the IGO's research network and the vast amount of 
research ongoing for the Millennium Development Project, Sen created an index that asserts to 
measure the extent to which each country's governance system to date has aided, or neglected, 
human development; this included the educational institutions it fostered over time. The HDI 
provides the pre -test data for this thesis, upon which the research questions are built. 

This thesis assumes that education is a public good, which is not universally held to be 
true. Education Expenditure data in this thesis does not account for private funding of private or 
public education, nor does it account for foreign aid for education. School choice, voucher pro- 
grams, charter schools, and lotteries were borne out of a perceived or real shortcoming of the 
system in place. Education may be better, more efficiently or more effectively delivered by the 
private sector (M. Friedman, 1997). 
Governance Corruption in Education 

Literacy rates, reporting the percentage of citizens with "basic reading, writing, and nu- 
meracy skills" (UNESCO, 2010d, p. 94), are insufficient to measure the complexity of education 
gained during school, and are often self -reported. The rate of graduation is equally insufficient to 
measure education earned, if graduates are not proficient readers or writers. Testing high on an 

65 



exam is an insufficient measure for application of knowledge on the job, or the degree to which 
knowledge and training matches the needs of employers (Romer, 1998a). Other factors to include 
gender differences (Barro & Lee, 2001), class size (D. J. Brewer et al., 1999), school choice (M. 
Schneider et al., 1997), school funding and competition (M. Friedman, 1997), and overall school 
quality (GMR, 2005), are neglected in the EAI. 

Gupta et al. (1998) report that by reducing the public resource pool, the remuneration for 
corruption could shift toward that which complements the endeavors of those that are corrupt and 
away from approved, sustainable economic development and market-demanded goods and ser- 
vices. Following are three such methods, or privilege-seeking behaviors. Bribes paid to school 
officials merely to gain entry into school (p. 10) is one method by which funds are transferred 
away from the public resource pool; by virtue of the official position of the bribe taker, this effec- 
tively increases the cost of education, creating a daunting financial barrier to "free" public 
education, and this income by -passes taxation. A second possible method is the under-delivery 
and over-pricing of supplies and textbooks, thereby increasing the effective cost of schooling and 
adversely affecting the demand (p. 11). Under-delivery decreases the quality and quantity of ed- 
ucation, with suppliers withholding shipment until sufficient bribes are paid (p. 12). The third 
method, because of school officials or other government employees having access to the budget, 
is siphoning, using various processes to create a profit center. One such mechanism is "under- 
invoicing", where part of the taxes collected for public education are pocketed (p. 6); another is 
"corruption and theft," where the service is charged to the government and either under provided 
or not provided to the public at all (p. 5). 

Education is measurable by its evidence, it is measured in human capital (Barro, 2001b; 
Lucas, 1988), and specifically, knowledge capital. Knowledge as capital, knowledge gains, posi- 
tive knowledge externalities, knowledge spillovers, and other adjectives that describe that 
transformation from the process of educating to gain a state of knowledge, or increases in intel- 

66 



lectual muscle, show up in economic growth generally, and individual income, specifically. 
Klingner & Sabet (2005) refer to knowledge, adaptation, and innovation as a "knowledge spiral" 
(p. 208). Romer (1990), consistent with Solow (1956, 1957), asserts that human capital, partially 
determined by knowledge, determines the rate of economic growth. 

It is because of this EAI shortcoming and shortcomings of other widely used measures 
for education attainment (e.g., test scores, literacy rates, graduation rates) that this thesis employs 
two measures for education. The first measure for education is an input -driven or supply-side 
measure, government spending on public education as a percentage of total government expendi- 
ture, or Education Expenditure (EE), using the precedent set by the United Nations in its report 
Education for All (GMR, 2010d). Many measures for education inputs exist, however, since this 
thesis uses GDP per capita as a measure for economic development, consistent with Sen (1997), 
La Porta & Shleifer (2008), and Gupta et al. (1998), we must use a derivative of GDP as the 
measure for a nation's spending on education to maintain data and construct validity across varia- 
bles, which the ICP network provides in the public data. The second measure for education is an 
outcome -driven measure, or demand-side, Income per capita (Ic), which serves as the proxy for 
learning, knowledge, and skill attainment consistent with a widely accepted and standard method 
since Kuznets (1934) (Barro & Lee, 1993; Galbraith & Kum, 2005; Gupta et al., 1998; Mauro, 
Abed et al., 2002; Schumpeter, 1939; Sen, 1984) 

Two scholars help frame debate on the effects of corruption on education. Both scholars 
argue that corruption is corrosive to a society; from the quantitative, empirical analysis discipline, 
Paulo Mauro, and from the qualitative, social capital discipline is Paulo Freire. Paulo Mauro 
(1998) Harvard Ph.D. and Fiscal Operations Division Chief for the IMF, asserts in Corruption 
and the Composition of Government Expenditure, ". . .there is significant evidence that corruption 
is negatively associated with government expenditure on education, and the relationship is robust 
to a number of changes in specification." He continues by arguing that the education line item 

67 



suffers relative to those more lucrative to rent-seekers. "The results are consistent with the hy- 
pothesis that education provides more limited opportunities for rent-seeking than other items 
do. . ." and that there "is also tentative evidence that the direction of the causal link is at least in 
part from corruption to the composition of spending" (Mauro et al., 2002). Scholars and econo- 
mists with research confirming Mauro include Armstrong (2005) Gupta et al. (2000), Pritchett , 
(1997), and Chua, (2006, 1997, 2001). 

The Paulo Freire Institute Headquarters is at the UCLA Graduate School for Education 
and Information Studies, which also houses the Freire Online, a Journal, dedicated to critical ped- 
agogy. Freire was awarded the UNESCO Prize of Education for Peace in 1986, and the 
International Development Prize by King Baudouin of Belgium in 1980 for his work in education 
and pedagogy. He also served as the Secretary of Education in Sao Paulo, and his "work has 
been the subject of hundreds of Ph.D dissertations." His work informs public policy through the- 
ories of social capital, critical theory, and social networks (Gadotti & Torres, 2005). In the 
seminal work on pedagogy in lesser-developed regions, Freire uncovers struggles against the 
tides of political, corrupt, philosophical, cultural, and social oppression infused into the educa- 
tional systems in communist counties. He asserts that the ineffectual education inhibits personal 
freedoms and the ability for the undereducated to be the 'owner of one's own labor' (1970, p. 
183). More critical is his assertion about motive. "The oppressor knows full well that this inter- 
vention [educating the oppressed] would not be to his interest. What is to his interest is for the 
people to continue in a state of submersion, impotent in the face of oppressive reality" (Freire, 
1970, p. 52). Furthermore, "I have already affirmed that it would indeed be naive to expect the 
oppressor elites to carry out a liberating education" (p. 135). 

Contrary to Mauro and Freire assertions about education under communist regimes, the 
evidence reveals robust education policy and delivery plans from the 1920s and through the 
1980s intended to create sustainable economic growth in the former USSR. The Technical and 



Vocational Education in the Union of Soviet Socialist Republics (Movsovic, 1959) written for 
UNESCO, reports the education plan than ensures free education, "the forms and methods of 
which are identical for the whole Soviet Union" (p. 14). Where the "role of the instructor. . . in 
the educational process is to inculcate into his pupils sound professional knowledge and work 
habits and develop creative initiatives and conscious labour discipline in them" (p. 19). The duty 
of the teaching staff is ". . .constantly to improve professional and pedagogical qualifications and 
to. . ..make careful preparation for classes. . ." (p. 19). The report continues, ". . .it is the function 
of every department in an institution of higher education not only to acquaint the student with the 
scientific bases of present-day industry but also to provide him with the solid scientific- 
theoretical grounding necessary for his future activity" (p. 44). 

Article 121: Constitution of the USSR establishes the right to education of the 
citizens of the Soviet Union. This right is given effect through the system of gen- 
eral and compulsory education,. . . general secondary education,. . .institutions of 
higher education and secondary vocational schools, and. . .frees technical educa- 
tion and training. . ..With all school education in the pupil's mother tongue (p. 4). 

Researchers such as Pritchett (2001), Levy (2007), and North (1991), attempt to reconcile 
the incongruous evidence. Between the USSR's the "enormous sums spent by the Soviet State on 
higher education and secondary vocational training" (p. 6) and research and development cele- 
brated as the hallmark in education planning for sustainable economic development, and its 
delivery over sixty years. However, growth waned increasingly toward the end of this period 
(Movsovic, 1959; Ofer, 1987). Ofer suggests that four factors contributed to the downward trend 
(1987, pg. 1812-1820). First is the inability of central planning to adapt to growing complexities 
in the world economy. Second is the increase in relative spending on defense versus technical in- 
novation. Third is the "weakening of the material incentive system. . .which in turn has negative 
effects on work motivation and efforts, thus further reducing growth" (p. 1815). Last is the effect 
of corruption. "A 'second economy' developing alongside the main, public sector takes another 



69 



bite from the effectiveness of the public sector" (p. 1816). Schneider refers to this economy as 
the Shadow Economy (Schneider et al., 2010a). 

Paradoxically, New Growth Theory follows both declarations in the Constitution of the 
USSR and the work of Mauro, Freire and others, affirming that education is a requirement of de- 
velopment, and deserves priority status in the public budgeting process (UNESCO, 2010d; 
Cortright, 2001). 

According to Mauro, "education turns out to be the only component of public spending 
that remains significantly associated with corruption when the level of per capita income in 1980 
is used as an additional explanatory variable" (2000, p. 10). However, underdeveloped countries 
have literate citizens. In fact, one of the reasons that the Educational Attainment Index (EAI) is 
problematic for the HDI is due to the lack of a better measure of education quality and quantity. 
Nearly every country has literacy rates near 99% (HDR, 2007, p. 226) corrupt or not, developed 
or not, with centrally planned or market-based economies. This fact alone calls for a different 
metric for gauging education broadly and literacy specifically. This fact also begs a different 
measure for the value of education as decided by the market for the sum of what a country's edu- 
cation public has thought of, innovated, created, engineered, developed, advanced, manufactured, 
and most importantly to the level of Gross Domestic Product per capita, what they have sold. 



70 



Summary 
Research Question 1 asks whether the change in Human Development Index accounts for 
the change in Income per capita. Hypothesis 1 maintains that the change in the HDI does not 
account for the change in Ic. Affirming this hypothesis opens the door to test whether the HDI 
together with a variable for the Shadow Economy better explains the change in Ic. Research 
Question 2 represents an attempt to inform the body of literature of the effect of corruption in 
these areas, specifically. Hypothesis 2 maintains that governance corruption, as measured by the 
SE, has a negative effect on Ic. Research Question 3 asks whether governance corruption, as 
measured by the Shadow Economy has a negative effect on Education Expenditures. Hypothesis 
3 maintains that the variation in EE can be explained by the variation in SE. If the tests affirm 
this hypothesis, the door is open to test the last research question. Research Question 4: Do the 
pre -test Human Development Index, governance corruption, and education expenditure together 
explain the change in Income per Capita? Hypothesis 4 maintains that there is a significant 
relationship between the change in Ic, HDIi 990 and change in EE. 



71 



CHAPTER 3 

THE DATA AND METHODOLOGY 

Methodology 
New Growth Theory suggests that both endogenous and exogenous factors encourage 
economic change, and that knowledge gains that support and invite research, development, tech- 
nology advances, and the skills to implement technology adoption are pivotal to creating healthy, 
sustainable economic development. The research design for this thesis is quasi-experimental. 
Equations 1 , 2, and 3 are foundational to Equation 4. Equation 1 tests the correlation coefficient, 
or the strength of association (Gujarati & Porter, 2009, p. 20). Equation 2 compares adjusted R 2 
between two linear regression equations. For Equation 3, the model is a linear regression, and the 
method is OLS. The key equation for this thesis is Equation 4, which compare the change in To- 
tal Income per Capita, Ic T , to the change in education expenditures (EE) given the HDI1990 
starting point. The model specified for this research is a Three -Variable Linear Regression using 
Ordinary Least Squares method. The model uses Ic T as the regressand, which depends on 
HDI1990, and EE as regressors (Yi = /?i + /?2^2i + /?3^3i /*{)• The test for this model using the 
Linear Regression Analysis is a hypothesis test to predict the Ale 3 per county. The equation run 
to predict AIc 3 follows (AIcT = Intercept + HDI 1990 +AEE3 + error). The approach to test 
the hypotheses is the test-of-significance approach. For test purposes, STATA requires the re- 
searcher to set the level of significance. The level of significance is set at the 95% confidence 
level, or 5% probability level of rejecting a hypothesis that is true. However, each test will identi- 
fy the p-value, or "the lowest significance level at which a null hypothesis can be rejected" 
(Gujarati & Porter, 2009, p. 122). 



72 



Hypothesis 

The key hypothesis this thesis tests is: governance corruption's effects on education 
through the public resource mechanism (Government Expenditure on Public Education as a per- 
centage of Total Government Expenditures) are direct and negative; the higher the degree of 
corruption, the lower the relative education budget per capita. Further, the lower the education 
budget per capita, the lower the relative individual income. 

Data 

The raw data are non-experimental, pooled, qualitative and quantitative, on a sample of 
30 countries occupying the Central and Eastern Europe, out of a current potential population of 
between 189 and 194 sovereign countries in the world plus 10 to 13 additional countries in transi- 
tional phases (depending on the changing political climate). All of the countries in the world are 
the population set. The transitional country pertinent to this study is Kosovo, which is reported as 
part of Serbia, as the declaration for separation of these two countries occurred after the 2008 
post -test year (See Country Briefs). To test the effects of governance corruption on education 
budgets and income per capita, we chose Central and Eastern European countries that offer 
unique data availability due to the focus by research scholars from international data agencies on 
the unprecedented events of the late 1980s marked by the dissolution of the Soviet Union. The 
data used herein have been quantified through indexing or econometric modeling, by the data 
purveyor, and were collected and input by hand, imported, or copied in a quantitative format. 
Each country has pre -test (1990) and post-test (2008) variables that are a nominal-scale, are dis- 
crete, and random. The data are linear (in the parameters, or i.e., not exponential) (Gujarati & 
Porter, 2009, p. 38). 

The data are secondary data, retrieved from three sources that are each contributors to es- 
tablished International Comparisons Program (ICP) (201 le) network of shared data. The first and 

73 



major source is human development data from the Human Development Project, part of the Unit- 
ed Nations Development Programme (UNDP). This program's research produces data, data sets, 
fact sheets, reports, and policy recommendations on human development, international and na- 
tional-level governance and public administration research. This research work supports the 
UNDP state and local governance programs, and other UN programs. The project attempts to 
gather data on every country, with success in procuring data for 166 countries. The data are col- 
lected through a combination of surveys, institutional data, government data, primary research on 
location, and sharing with ICP. The resulting qualitative and quantitative data are indexed from 1 
to 100 (HDR, 2008c). The HDI is a composite index composed of three indices; the Educational 
Attainment Index (EAI); the Life Expectancy Index (LEI); and the Gross Domestic Product Index 
(GDPI). When GDP is divided by population, the resulting figure is Income per Capita, or Indi- 
vidual Income Ic. In other words, the HDI = (1 x LEI) + (1 x EAI) + (1 x Ic). Each component 
index is developed from its component data. The composite HDI and these component indices 
are the key variables used. The source for each data point used is noted on Table D, found in the 
Appendix. 

The second source for data is the Shadow Economy (Schneider et al., 2010b) data, which 
provides figures measuring that GDP produced and not counted in National Income Accounting. 
The sources for data on 162 countries included a combination of surveys, institutional data, gov- 
ernment data, primary research, and data shared through the. This data set covers all of the 
sample set of countries, missing only three data points, Turkey and Mongolia in 1990, and Turk- 
menistan in 2007. These three countries were studied individually, and the data for these is 
available using the same methods (Eilat & Zinnes, 2000; Yereli et al., 2007; Zhou, 2007). The 
method preferred by Schneider is the MIMIC method (DellAnno & Schneider, 2006), which is a 
simultaneous equation model sets qualitative and quantitative data into an equation as inputs and 



74 



outputs productivity to derive the percentage of the official economy lost to unofficial productivi- 
ty. 

Comparisons of nine common methods for calculating national-level governance corrup- 
tion (e.g. WGI, TI, CPI, GCB, BPI) are found in Table A (Schneider & Enste, 2000). Many other 
methods exist for sub-national, firm level (e.g., BEEPS, EBRD), and other forms of corruption. 
Dell'Anno & Schnieder state, "[t]here does not exist any commonly accepted methodology for 
estimating the underground economy. The estimates are always subjective and depend on the 
quality of the dataset the methods applied and the subjective decisions of the researcher. Shadow 
Economy estimates are never very stable and absolute. . ." (2006, p. 16). The authors go on to 
support the MIMIC method by asserting that the MIMIC is the better of the known methods for 
calculating national-level governance corruption relative to productivity on the books (F. G. 
Schneider & Enste, 2000) (see Figure 1. MIMIC Model below). 



Transfers and 

Subsidies 



Government 
Coosuniption 



Government 
Effectivenew. 






- 


Fiscal 
Freedom 





Bureaucracy 
Costs 



Rule of Law 



Labor Force 
Participation 




X^ Ratio MQ to 
Ml 



Real GDP per 
/ Capita 





►- 


Bribes 





\ 



Judicial 
Independence 



Figure 5 Shadow Economy MIMIC Model. 



75 



For the purposes of this thesis, in order "[t]o get information about the dynamics and size 
of the Shadow Economy, the MIMIC model is still one of the best approaches to this purpose" (p. 
16). Since Schneider's data have the best coverage on the sample set of counties and since it uti- 
lized the most appropriate measurement method for our purpose (which is the Shadow Economy 
size), Schneider's data is the better choice overall. 

The third source of data is the Estates project, for the Education Expenditure data. Es- 
tates is a joint international research group for the UN, through UNESCO, the World Bank 
Research Group, and the ICP. Yearly data publications such as the Global Education Digest 
(GED) provide a catalog of statistics. Data for the 2008 Education Expenditures are found in the 
2008 GED, Table 13, Public Expenditure and Expenditure on Education by Nature of Spending 
(pp. 167-176). The statistic used in this thesis is the Public Education Expenditure (EE) as a Per- 
centage of Total Government Expenditure. In a major advancement, the UN, through its 
Statistical Information System on Expenditure in Education (SISEE), requested yearly data pro- 
curement as of 1998. From 1970 to 1990, United Nations Children's Fund (UNICEF) gathered 
the official data every five years, and added data to the set in the off years when it met all of the 
previous methodology criteria. Researchers and the ICP still use the earlier data and deem it as 
reliable (UNECSO, 1998). Data for education statistics deemed reliable based on the new meth- 
odology became available in 1998. Important here is the dearth of data that exist from 1986 
through 1998; only 54 data points exist for these eight years for the entire world, and only seven 
of these are readings for the sample set. 



76 



Reliability and Validity Testing 

See Data Reliability and Validity Testing in the Appendix. 
Research Question 1 

Recall that which Kuznets, Sen, and others assert is Official GDP per capita, Ic, may not 
be a sufficient proxy for individual human development, as it lacks variables such as individual 
welfare, living standard, or earning capability (HDR, 2008c, p. 225; Kuznets, 1934; Sen, 1984). 
Klenow & Rodriguez-Clare (2005, p. 833) and others assert the evidence from many scholars 
employing various models are consistent in that less than half of the variation in individual in- 
come can be attributed change in human capital and development (Easterly & Levine, 2001; 
Klenow & Rodriguez-Clare, 1997; W. K. Wong, 2007a). In addition, the HDI is a widely accept- 
ed proxy for the stock of human capital (HDR, 1990; Sen, 1997; W.-K. Wong, 2007b) 

Research Question 1.1: Are the HDI and the change in the Total Income per Capita cor- 
related at .5 or higher? To test this construct with our data, we can run the correlation coefficient 
test. If we reject the null hypothesis, then we can conclude for now, that the correlation between 
the Human Development Index from 1990 to 2008 and the change in Total Income per Capita, 
(Ic T ), is less than .5, consistent with the rule used in Wong, (2007b). 

Hypothesis 1.1: The correlation coefficient of AIc T from 1990 to 2008 and AUDI from 
1990 to 2008 is less than .5. 

Hypothesis 1.2: The correlation coefficient of AIc T from 1990 to 2008 and AHDI compo- 
nent indices, ALEI + EAIfrom 1990 to 2008 is less than .33. 

This test of the changes in the independent component indices is important for several 
reasons. Sen contends that the sum total of the factors human development over time are cap- 
tured in a snap shot in time measured by the HDI composite index made up of three component 
indices, EAI, LEI, and GDPI. Using the HDI, then, we respect and factor into the equation the 
sum of history for each country, or the proxy human capital and development stock at one point 

77 



in time, consistent with the recent literature (Klenow & Rodriquez-Clare, 2005; Wong, 2007). 
Examining the change equalizes the pre -test differences in countries. 

Gujarati & Porter, (2009) explain that a properly specified model will yield an intercept 
term. The intercept may be statistically equal to zero, which means that it runs through the origin, 
or close to it; however, the near-zero intercept is a product of the regression equation. In the case 
of 30 Eastern and Central European countries, the regression equation will yield an intercept 
term. This point on the Y-axis is the starting point for calculating the effects of governance cor- 
ruption on education budgets and income per country. Without "very strong a priori 
expectation," according to Gujarati & Porter (2009), "one would be well advised to stick to the 
conventional, intercept-present model" (p. 150). One example of a strong a priori expectation of 
a zero-intercept model exists here. In equation 4, one would expect that where human develop- 
ment is zero, income would also be zero, justifying a zero-intercept model (Gujarati & Porter, 
2009, p. 150). In addition, one can check for misspecification of a model after the fact by check- 
ing the statistical significance of the constant, to verify that there are no omitted variables (p. 
198). For example, the greatest difference in the pre -test HDI, or HDI1990, in the Central and 
Eastern Europe is .263 points, from Tajikistan at .636 to Austria at .899. The post-test HDI, or 
HDI2007, for these two countries is .688 and .955 respectively, or .267 points apart. Tajikistan's 
HDI increased .0818 points, while that of Austria increased .0623. Examining the change in HDI 
shows that the sum total of the change in economic and demographic data underlying Tajikistan's 
HDI increased at a faster pace than did Austria, .015% faster. 

According to Gujarati & Porter, (2009), analyzing the change in our variables minimizes 
the chances of heteroscedasticity, autocorrelation, and multicolinearity naturally present in pooled 
(cross-country, time-series) data. Heteroscedasticity is the unequal variances due to errors, outli- 
ers, inertia, skewness, or incorrectly specified linear regression (pp. 365-368). Autocorrelation is 
"correlation between members of a series of observations ordered in time or space. . .and does not 

78 



exist in the u" (p. 413). Multicolinearity is "similar linear relationships among some or all ex- 
planatory variables" (p. 321) naturally present in pooled data. Gujarati Porter, (2009, p. 434) 
suggests the Durban Watson d-test cannot be used to detect serial correlation, in the equations 
containing SE or EE data, as data points are missing from both sets of data. It cannot be used on 
the HDI data or the SE data, as the data have lagged variables. 

This examination may inform many policy questions. Two sample policy questions spe- 
cific to this thesis are: "Which element of the development data had the greatest impact on 
Tajikistan's change in income per capita?" alternatively, "What component measure of Moldo- 
va's HDI shows the least progress over time, and what can a policy change do to fix it?" 

The thesis' purpose is to measure the effects of corruption on education budgets and in- 
come per capita as measured by the capability of an individual to earn income. This brings us to 
the second research question. 
Research Question 2 

Does the corruption, as measured by the Shadow Economy, negatively affect Income per 
Capita? This question requires us to add the first new data point, corruption as measured by the 
SE. Recall that the SE is stated as a percentage of the official GDP (Schneider et al., 2010b). 
The SE set of data is one of several that assert to measure the size of GDP lost to corruption, hid- 
den in the underground or unofficial markets. The only such study with adequate coverage of 
Central and Eastern Europe was sponsored by the World Bank and published in 2010, Shadow 
Economies all over the World: New Estimates for 162 Countries from 1999 to 2008. 

We employ an OLS regression to test our second research question. We will regress the 
AIc and the HDI 199 o, and then add a variable for explanatory power. If the theoretical construct 
is valid, the relationship between AIc and HDI 1990 will be statistically significant; and the SE 
variable will add explanatory power to the regression making it a more robust predictor of AIc . 
Consistent with widely accepted growth regression models, maintaining a constant in a compari- 

79 



son of equations measuring goodness of fit (adjusted R 2 ) expresses that the pretest starting point 
was not zero; this shows the value of the pretest stock of Human Development based on the OLS 
regressions (Barro, 2001b; Cobb & Douglas, 1928; Klenow & Rodriquez-Clare, 2005; Mauro, 
Abed et al., 2002; Pritchett, 2001). 

Hypothesis 2: The adjusted R resulting from an OLS regression of pretest HDI against 
the AIc is equal to or greater than the adjusted R resulting from an OLS regression of pretest 
HDI and SE 2 oos against the AIc . 

If we reject the null hypothesis, we must conclude for now that the SE 2 oo8 per country in- 
cluded in the regression with the HDI1990, explains more of the variation in the AIc 3 than does the 
HDI1990 alone. This finding would be consistent with the theory that corruption hinders economic 
development, and of the findings of Schneider et al. (2010), Kauffmann et al. (2008), Johnston, 
(2007), and other scholars. 
Research Question 3 

Does a change in the Shadow Economy negatively affect Education Expenditure? To test 
the effects of corruption on education budgets, we employ our final new variable, Education Ex- 
penditure (EE). If the presence of the SE has no significant effect on the EE, then we find that the 
theoretical construct was invalid. The null hypothesis states that AEEc does not equal the ASE. 
Gupta et al. (1998, p. 28), and others assert the evidence from many scholars employing various 
models are consistent in that less than one third of the variation in individual income can be at- 
tributed directly to the change in corruption (Kaufmann, 2003; Pritchett, 2001). In addition, the 
HDI is a widely accepted proxy for the stock of human capital (HDR, 1990; Sen, 1997; Wong, 
2007) 

Hypothesis 3: The variation in the AEEc from 1990 to 2008 is not explained by the varia- 
tion in SE2008. 



80 



If we reject the null hypothesis, that the effects of the SE on EE are statistically signifi- 
cant, then we can conclude for now that a relationship exists. If our working theory about 
corruption's effects on education budgets prevails, and an increase in the size of the SE results in 
a decrease in education funding, we can measure this decrease, and measure its effect, if any, on 
income. 
Research Question 4.1 

Do the pre -test HDI, governance corruption, and education expenditure together explain 
the change in Income per capita? The null hypothesis asserts that there is no significant relation- 
ship between the Official Income per Capita, AIc , and the explanatory variables, HDI 1990 , and the 
change in EE per capita from the pretest to the posttest values, EEC1990 and EEc 2 oo8. 

Hypothesis 4.1: The variation in the AIc from 1990 to 2008 is explained by the varia- 
tion in the HDljggo, and the change from EEC3 in ;ggo and EEC3 in 200s- 

If we reject the null hypothesis, we can assume for now that a relationship between the 
pre and post-test per capita figures for EE 3 , and the AIc exists. 
Research Question 4.2 

The null hypothesis asserts that there is no significant relationship between the Unofficial 
Income per Capita, AIcu, and the explanatory variables, HDI1990, change from the EE per capita 
pretest the and posttest values AEEc 

Hypothesis 4.2: The variation in the AIc3from 1990 to 2007 is explained by the varia- 
tion in the HDI 1990, and the change in EEc3 from 1990 to 2008. 
Research Question 4.3 

The null hypothesis asserts that there is no significant relationship between the AIc y and 

the explanatory variables, HDI1990, the change AEE 3 

Hypothesis 4.3: The variation in the AIc T from 1990 to 2008 is not explained by the variation in 

the HDI 199 o, and the change in EEc from 1990 to 2008. 

81 



Summary Statistics 



Variable | 


Ob 

_|_ 


Mean 


Std. Dev. 


Min 




Max 


HDI1990 


30 


.78 


.0575506 


.636 




.896 


HDICh 


30 


.0507633 


.0283926 


-.0204 




0918 


LEI EAI 


30 


130.0245 


20.85304 


103.895 


183.504 


IcChDc 


30 


1218.467 


1702.272 


-1194 




6119 


SE1990 


30 


27.42 


8.127662 


12.2 




45.1 


SE2008 


30 


38.95233 


11.65943 


16.1 




68.8 


SE2008DC 


30 


1287. 911 


1022. 685 


109 




4113 


IcTotalChDc 


30 


1824.1 


2225.068 


-1382 




7862 


EEDc2008 


30 


551.7297 


554.2541 


46.02128 


2397.184 


EEChDc 


30 


123.391 


266.0138 


-272.1115 


890 


7047 



82 



CHAPTER 4 

ANALYSIS 

Research Question 1 
Research Question 1.1: Are the Human Development Index and the Change in the In- 
come per capita correlated at .5 or higher? To test this construct with our data, we can run the 
correlation coefficient test. If we reject the null hypothesis, then we can conclude for now that 
the correlation between the Human Development Index from 1990 to 2008 and the change in In- 
come per Capita is less than .5, consistent with the rule used in Wong, (2007b). 
Summary Statistics 

Variable | Obs Mean Std. Dev . Min Max 

+ 

HDICh | 30 .0507633 .0283926 -.0204 .0918 

LEI_EAI | 30 130.0245 20.85304 103.895 183.504 

IcChDc | 30 1218.467 1702.272 -1194 6119 

Correlation Coefficient 

| IcChDc HDICh LEI EAI 

+ 

IcChDc | 1.0000 
HDICh | 0.5005 1.0000 
LEI_EAI | 0.6793 0.2639 1.000 

Hypothesis 1.1: The correlation coefficient of Ale from 1990 to 2008 and AUDI from 
1990 to 2008 is less than .5. 
Equation 1.1 

Null Hypothesis: H : if \t\ > ta n _ 2 : reject H 

2' 

Maintained Hypothesis: Hi: if \t\ < ta n _ 2 : fail to reject H 

2' 

The correlation coefficient is .4599, which is less than the benchmark of .5. On a one- 
tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 degrees of 
freedom at the 95% confidence level. For now, we maintain that the correlation between the 



83 



change in the variables meets the test requirement, at less than the benchmark. Below, the scatter 
graph shows the correlation. 





3 
Z> 
Z> 

o 
o 
o 

CO 

o 
o 
o 

CO 

o 
o 
o 
Tf 

o 
o 
o 

CM 

o - 

z> 
z> 
z> 

M 


Change in Income per Capita vs 
Change in Human Development Index 












A 




A 


A * 


A ■ _ 




■ ■ A a*a a A 

. ■ ■ 


■ ■ A 
A ■ 








1 1 1 1 1 1 

-.02 .02 .04 .06 .08 
Human Development Index % Change from 1990 to 2008 


i 
.1 






a Eastern Bloc, non USSR ■ Former USSR 
Correlation Coefficient = .4599 

















Figure 1.1 Change in Income per Capita and Change in Human Development Index. 



To test the correlation between the Life Expectancy Index and the Educational Attain- 
ment Index, equally weighted (the weights in the HDI are equally weighted), we take the GDP 
index out, and re -run the correlation coefficient. 

Hypothesis 1.2: The correlation coefficient of Ale from 1990 to 2008 and AUDI compo- 
nent indices, ALEI + EAIfrom 1990 to 2008 is less than .5. 
Equation 1.2 

Null Hypothesis: H : if \t\ > ta n _ 2 ; reject H 

2' 

Maintained Hypothesis: Hi: if \t\ < ta n _ 2 : fail to reject H 

2' 

The correlation coefficient is .0552, which is significantly less than the benchmark of .5. On a 



one-tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 degrees 



84 



of freedom at the 95% confidence level. For now, we maintain that the correlation between the 
change in the HDI is very slightly negatively correlated with the change in Ic, at -.0015. Below is 
the scatter graph depicting the correlation between the Change in Income per Capita and the life 
expectancy and educational attainment indices. 



Life Expectancy Index + Educational Attainment Index 
vs Income per Capita 



o 
o 



O 



o 
o 



o 
m 



A ■ 



-2000 



1 1 1 

2000 4000 

Change in Income per Capita from 1990 to 2008 



6000 



Former USSR * Eastern Bloc, non USSR 



Correlation Coefficient - .0552 



Figure 1.2 Change in Income per Capita and Change in LEI and EAI. 



85 



Research Question 2 

Does governance corruption negatively affect Individual Income? (Governance corrup- 
tion is measured by the average Shadow Economy from 2000-2008, and Education expenditure is 
measured with the proxy EEc. A linear regression comparison of the R 2 tests Research Question 
2, using the Change in Income per Capita ffi C i a i , AIc , as the dependent and HDI 1990 as the inde- 
pendent variable. HDI 1990 is the pre-test or legacy measure, the starting point in human 
development measurements, for the sample set of countries. 

Hypothesis 2: The adjusted R resulting from a linear regression ofHDI against the AIc 
is higher than the adjusted R resulting from a linear regression ofHDI and SE 2 oos against the 



mc . 








Equation 2. 1 








Null Hypothesis: Ho : 


AIc ^HDIisgo 






Maintained Hypothesis 


: H i: AIc o =HDI 1990 






Summary Statistics 








Variable | Obs 


Mean Std. Dev . 


Min 


Max 


HDI1990 | 30 


.78 .0575506 


.636 


.896 


Icl990 | 30 


3010.067 3628.822 


426 


19428 


SE2008 | 30 


38.95233 11.65943 


16.1 


68.8 



Correlation Coefficient 

| HDI1990 Icl990 SE2008 

+ 

HDI1990 | 1.0000 
Icl990 | 0.6886 1.0000 
SE2008 | -0.5148 -0.5977 1.0000 



86 



Test: Linear Regression 95% Confidence Level 

Regressed dependent variable AIc using independent variable HDI 19 9 

Source | SS df MS Number of obs = 30 
+ F( x, 28) = 20.84 

Model | 35855179.2 1 35855179.2 Prob > F = 0.0001 
Residual | 48178968.3 28 1720677.44 R-squared = 0.4267 
+ Ad j R-squared = 0.4062 

Total | 84034147.5 29 2897729.22 Root MSE = 1311.7 

IcChDc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 19320.9 4232.54 4.56 0.000 10650.93 27990.86 
_cons | -13851.83 3310.056 -4.18 0.000 -20632.17 -7071.489 

Post-Estimation Statistics for Regression 

White's test for Ho: homoscedasticity 

against Ha: unrestricted heteroscedasticity 

chi2(2)= 8.62 
Prob>chi2= 0.0134 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 8.62 2 0.0134 
Skewness | 4.37 1 0.0365 
Kurtosis | 0.58 1 0.4452 
+ 

Total | 13.58 4 0.0088 

Ramsey RESET test using powers of the fitted values of IcChDc 
Ho: model has no omitted variables 
F(3, 25) = 5.95 
Prob > F = 0.0033 

Information Criteria 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 30 -265.2512 -256.9067 2 517.8134 520.6158 

The regression output shows an F-score at 20.84 with 29 degrees of freedom, at most, 

40.62 % of the variation in the AIc can be explained by the variation in the HDI1990, and the t- 

value of the HDI relationship is very significant at 4.56. This test passes the "2-f Rule of 

Thumb." The RMSE is 131 1.7. "The minimum MSE criterion consists in choosing an estimator 

whose MSE is the least in a competing set of estimators. . .there is a trade-off involved - to obtain 

minimum variance, you may have to accept some bias" (Gujarati & Porter, 2009, p. 828). 

87 



White's test confirms autocorrelation with X 2 of 8.62 on 2 degrees of freedom. The IM-test con- 
firms left skewed data at 4.37 and a short and fat (platykurtic) kurtosis distribution at .58. The 
AIC is 517.8. The analysis suggests rejecting the null hypothesis, confirming a significant rela- 
tionship. Next, we compare the R 2 values between here and a second equation adding SE 2 oo8 as 
an explanatory variable. 
Equation 2.2 

Null Hypothesis: 

H : R regress AIco with HDliggo > R regress AIco with HDliggo and SE200S 

Maintained Hypothesis: 

H! : R 2 regress AIc with HDI 1990 <R 2 regress AIc with HDI 1990 and SE 2 oos 
Summary Statistics 



"ariable | 


Obs 


Mean 


Std. Dev. 


Min 


Max 


HDI1990 | 


30 


.78 


.0575506 


.636 


.896 


SE2008 | 


30 


38.95233 


11.65943 


16.1 


68.8 


IcChDc | 


30 


1218.467 


1702 .272 


-1194 


6119 



Correlation Coefficient 

I HDI1990 IcChDc SE2008 

+ 

HDI1990 I 1.0000 
IcChDc I 0.6532 1.0000 
SE2008 I -0.5148 -0.5981 1.0000 

Test: Linear Regression95% Confidence Level 

Regressed dependent variable AIc using independent variables HDI l990 and SE 2 oo8- 

Source | SS df MS Number of obs = 30 

F( 2, 27) = 14.62 
Model I 43690767.5 2 21845383.8 Prob > F = 0.0000 
Residual | 40343379.9 27 1494199.26 R-squared = 0.5199 
+ Ad j R-squared = 0.4844 

Total | 84034147.5 29 2897729.22 Root MSE = 1222.4 

IcChDc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 13897.18 4600.658 3.02 0.005 4457.41 23336.95 

SE2008 | -52.00247 22.70871 -2.29 0.030 -98.5969 -5.40805 

cons | -7595.717 4120.428 -1.84 0.076 -16050.14 858.7039 



Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 
against Ha: unrestricted heteroscedasticity 

chi2(5)=6.43 
Prob > chi2=0.2670 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 
+ 

Heteroskedasticity | 6.43 5 0.2670 
Skewness | 2.08 2 0.3536 
Kurtosis | 2.56 1 0.1097 

+ 

Total | 11.06 8 0.1981 

Ramsey RESET test using powers of the fitted values of IcChDc 
Ho: model has no omitted variables 
F(3, 24) = 3.44 
Prob > F = 0.0328 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 30 -265.2512 -254.2443 3 514.4885 518.6921 



The regression output shows a lower, yet still significant, F-score at 14.62 with 29 de- 
grees of freedom. At most, 48.44% of the variation in the AIc can be explained by the variation 
in the pre-test HDI, and both f-values of the HDI 1990 and SE 2 oo8 variables are high and significant 
at 3.026 and -2.29. This test passes the "2-f Rule of Thumb". The RMSE is lower at 1222.4. 
White's test rejects autocorrelation with X 2 of 6.43 on 5 degrees of freedom. The IM-test con- 
firms a left skewed data at 2.08 and less platykurtic at 2.56. The AIC is lower, at 514.4885, 
which is preferred to the higher in Equation 2.1 of AIC 517.8134. Akaike's Information Criteria 
(AIC) states that when "comparing two or more models, the model with the lowest value of AIC 
is preferred" (p. 494). The analysis suggests rejecting the null hypothesis, confirming a signifi- 
cant relationship on the second equation. 

A comparison of the R 2 test suggests rejecting the null hypothesis, and confirming for 
now that the R 2 of the augmented, second equation is higher, from 40.62% to 48.44%. In addi- 
tion, the entire equation is more robust with a lower RMSE, lower AIC, less skewness, and no 

89 



autocorrelation. The F-score, which is lower yet still high, explains that the shape of the distribu- 
tion is flatter. The rejected hypothesis suggests a temporary conclusion in favor of the SE 2 oo8 per 
country included in the regression with the HDI1990, explains more of the variation in AIc than 
does the HDI 1990 alone. This finding would be consistent with the theory that corruption hinders 
economic development, and of the findings of Schneider et al. (2010), Kauffmann et al. (2008), 
Johnston, (2007), Mauro (1998b) and other scholars. 



90 



Research Question 3 

Does governance corruption negatively affect future Education Expenditure? (Govern- 
ance corruption is measured by the average Shadow Economy from 1990-1999, and Education 
Expenditure is the average from 2000-2008,(EE$c 2 oo8))- A linear regression tests the effects of 
corruption on EE$c 2 oo8, by setting the EE$c 2 oo8 as the dependent variable and the Shadow Econ- 
omy in 1990, SE 199 o, as the dependent variable. 

Hypothesis 3: The variation in the EE$c 2 oosis not explained by the variation in SE 19 g . 
Equation 3 

Null Hypothesis: H : EE$C2oog^ : SEi 99 o 

Maintained Hypothesis: Hi : EE$C2oo8= SE1990 
Test: Linear Regression 95% Confidence Level 
Summary Statistics 

Variable | Obs Mean Std. Dev . Min Max 

+ 

SE1990 I 30 27.42 8.127662 12.2 45.1 

SE2008 I 30 38.95233 11.65943 16.1 68.8 

EEDc2008 I 30 551.7297 554.2541 46.02128 2397.184 

Correlation Coefficient 

I SE1990 SE2008 EEDc2008 

+ 

SE1990 I 1.0000 
SE2008 I 0.8359 1.0000 
EEDc2008 I -0.4450 -0.5807 1.0000 

Regressed dependent variable EEc 199 o using independent variable SE1990 

Source | SS df MS Number of obs = 30 
+ F( lf 28) = 6.91 

Model I 1764361.29 1 1764361.29 Prob > F = 0.0137 

Residual | 7144369.59 28 255156.057 R-squared = 0.1980 

+ Ad j R-squared = 0.1694 

Total | 8908730.88 29 307197.616 Root MSE = 505.13 

EE$c 20 08 I Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

SE1990 | -30.34793 11.54086 -2.63 0.014 -53.98832 -6.707546 
cons | 1383.87 329.6151 4.20 0.000 708.6841 2059.056 



91 



Does governance corruption negatively affect current Education Expenditure? (Govern- 
ance corruption is measured by the average Shadow Economy from 2000-2008, and Education 
Expenditure is the average from 2000-2008,(EE$c 2(K )8))- A linear regression tests the effects of 
corruption on EE$c 20 o8, by setting the EE$c 2 oo8 as the dependent variable and the SE 2 oo8 as the de- 
pendent variable. 

Hypothesis 3: The variation in the EE$c 2 oosis not explained by the variation in SE 2 oos- 
Equation 3 

Null Hypothesis: H : EE$c 20 o8t^ SE 200 8 

Maintained Hypothesis: Hi : EE$c 2 oos= SE 20 o8 
Test: Linear Regression 95% Confidence Level 
Regressed dependent variable EEc 1990 using independent variable SE 200 8. 

Source | SS df MS Number of obs = 30 
+ F( x, 28) = 14.24 

Model | 3003721.27 1 3003721.27 Prob > F = 0.0008 

Residual | 5905009.61 28 210893.2 R-squared = 0.3372 

+ Ad j R-squared = 0.3135 

Total | 8908730.88 29 307197.616 Root MSE = 459.23 



EEDc2008 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

SE2008 | -27.60283 7.314002 -3.77 0.001 -42.58489 -12.62078 
cons | 1626.924 296.9787 5.48 0.000 1018.591 2235.258 



The test results suggest rejecting the null hypothesis and concluding for now that the ef- 
fects of the Shadow Economy on the Education Expenditures per person stated in dollars, SE 20 08 
on EE$c 20( )8 are statistically significant. In addition, 31.35% of the variation in Education Ex- 
penditures can be explained by variation in the Shadow Economy. The F-score is 14.24 with 29 
degrees of freedom and the t-value is -3.77 for SE 20 os- The RMSE is low, at 459.23. This test 
passes the "2-? Rule of Thumb." Figure 3.1 shows the effects of the Shadow Economy on future 
Education Expenditures. Figure 3.2 shows the effects of the Shadow Economy on current Educa- 
tion Expenditures. 

92 



Shadow Economy's Effect on Future Education Expenditures 



4000- 



3000- 



2000- 



1000 



■ Former USSR 

* Eastern Bloc, non USSR 

— Fitted values 
Correlation Coefficient = -.631 4 




10 20 30 40 50 

Shadow Economy % of GDP Average from 1990 to 1999 



Figure 3.1 Shadow Economy's Effects on Future Education Expenditures. 



Shadow Economy and Current Education Expenditures 



Former USSR 
* Eastern Bloc, non USSR 
— Fitted values 
Correlation Coefficient = -.6679 




10 20 30 40 50 60 

Shadow Economy % of GDP Average 2000 - 2008 



70 



Figure 3.2 Shadow Economy's Effects on Current Education Expenditures. 



93 



Research Question 4 

Do the pre -test HDI, governance corruption, and education expenditure explain the 
change in Income per Capita? The question is tested in three ways. (4.1) do these variables ex- 
plain Official Income per Capita? (4.2) Do the pre -test HDI, governance corruption, and 
education expenditure explain the change in Unofficial Income per Capita? (4.3) Do the pre-test 
HDI, governance corruption, and education expenditure explain the change in Total Income per 
Capita? (Corruption is measured by the average Shadow Economy as a percent of official GDP 
from 2000-2008, and education expenditure is measured with the proxy AEEc). 
Hypothesis 4.1 

The null hypothesis asserts that there is no relationship between the change in Official In- 
come per Capita, AIco, and two explanatory variables, (1) the change in Education Expenditure 
Dollars per Capita between the EEc pretest and the posttest values, EEci 990 and EEc 2 oos- Gujarati 
and Porter (2009) explain and supports the practice of adding variables to seek higher degrees of 
significance and better over-all fit (pp. 474-475). 

Hypothesis 4.1: The variation in the AIc from 1990 to 2008 is not explained by the var- 
iation in the HDI 1990 the AEEc from 1990 to 2008. 
Equation 4. 1 

Null Hypothesis: H : AIc ^ HDI 19 9 + AEEC1990.2008 

Maintained Hypothesis: Hi : AIc = HDI1990 + AEEC1990.2008 
Summary Statistics 

Variable | Mean Std. Dev. Min Max 
+ 

Alc | 1218.467 1702.272 -1194 6119 

HDI 1990 | .78 .0575506 .636 .896 

AEEc | 123.391 266.0138 -272.112 890.705 



94 



Correlation Coefficients 

| HDI1990 EEChDc IcChDc 

+ 

HDI1990 | 1.0000 
EEChDc | 0.4165 1.0000 
IcChDc | 0.6532 0.4723 1.0000 

Test: Linear Regression 95% Confidence Level 

Regress dependent AIc with independent variables HDI 1990 , EEA$/c 1990 _2oo8. 

Source | SS df MS Number of obs = 30 
+ F( 2 , 28) = 14.83 

Model | 66140725.8 2 33070362.9 Prob > F = 0.0000 

Residual | 62433252.2 28 2229759.01 R-squared = 0.5144 

+ Ad j R-squared = 0.4797 

Total | 128573978 30 4285799.27 Root MSE = 1493.2 

AlcO | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 1182.643 390.9868 3.02 0.005 381.7431 1983.543 
EEChDc | 2.821598 1.057342 2.67 0.013 .6557301 4.987465 

Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 

against Ha: unrestricted heteroscedasticity 
chi2(5) = 14.36 
Prob>chi2 = 0.0135 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 14.36 5 0.0135 
Skewness | 3.12 2 0.2099 
Kurtosis | 0.34 1 0.5590 
+ 

Total | 17.83 8 0.0226 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

.1 30 . -260.7943 2 525.5886 528.391 

The regression output shows an F-score of 14.83 with 30 degrees of freedom. At most, 
47.97% of the variation in the dollar change in total income per capita can be explained by the 
variation in the independent variables. The ?-values are significant. This test passes the "2-f Rule 
of Thumb" as both the HDI and Education Expenditure f-scores are greater than 2.0 (Gujarati & 



95 



Porter, 2009). The RMSE is 1493.2. The White's General Test for Heteroscedasticity reports a 
critical X 2 value of 14.36 which exceeds the X 2 score of 5 degrees of freedom, and which means 
heteroscedasticity exists (p. 387). The IM-test confirms slightly left skewed data at 3.12, and a 
slightly platykurtic at .34. The AIC is 525.5886. The analysis of the equation suggests rejecting 
the null hypothesis, confirming for now that a statistically significant relationship exists. 

The results of this test suggest that the change in Official Income per Person over the 18- 
year test period is a function of the human development starting point in 1990 (HDIi 990 ), and the 
change in percentage of the total expenditure budget set aside per person for public education in 
the pretest and posttest years (EEc 1990 and EEc 2 oos)- However interesting these results, the change 
in Official Income per Capita, Ic , does not account for the change in Unofficial, or Shadow 
Economy Income per Capita, which may yield more informative results, (Gujarati & Porter, 
2009). (See Table: 4.1 in the Appendix). 

Testing this equation using the change in the official dollars earned, however, will tend to 
provide a skew in the results that captures bigness in the available official income, and not the 
distribution of that income to the individual, only the average distribution of official income per 
capita. This can be seen in Table 4, on the graphic comparison of these equations. (See Table: 
4.1 in the Appendix). 
Hypothesis 4.2 

The null hypothesis asserts that there is no relationship between the change in Unofficial 
Income per Capita, Alcy, and two explanatory variables, (1) the change in Education Expenditure 
Dollars per Capita between the EEc pretest and the posttest values, EEc 1990 and EEc 2 oos- Gujarati 
and Porter (2009) explain and supports the practice of adding variables to seek higher degrees of 
significance and better over-all fit (pp. 474-475). 

Hypothesis 4.2: The variation in the Ale v from 1990 to 2008 is not explained by the var- 
iation in the HDI iggo the AEEcfrom 1990 to 2008. 

96 



Equation 4. 1 

Null Hypothesis: H : AIcu ^ HDI 1990 + AEEc 1990 .2oo8 

Maintained Hypothesis: HI: AIcu = HDI 1990 + AEEci 990 _2oo8 
Summary Statistics 

Variable | Mean Std. Dev . Min Max 

+ 

IcChDc | 1218.467 1702.272 -1194 6119 

HDI1990 | .78 .0575506 .636 .896 

EEChDc | 123.391 266.0138 -272.112 890.705 

Correlation Coefficients 

| HDI1990 EEChDc IcChDc 

+ 

HDI1990 | 1.0000 
EEChDc | 0.4165 1.0000 
IcChDc | 0.6532 0.4723 1.0000 

Test: Linear Regression 95% Confidence Level 

Regressed dependent variable AIcu using independent variables HDIi 990 , and AEEc 

Source | SS df MS Number of obs = 30 
+ F( 2, 28) = 42.64 

Model | 60294328.5 2 30147164.3 Prob > F = 0.0000 

Residual | 19797817.4 28 707064.907 R-squared = 0.7528 

+ Ad j R-squared = 0.7352 

Total | 80092145.9 30 2669738.2 Root MSE = 840.87 

Alcul Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990I 1411.18 220.1724 6.41 0.000 960.1775 1862.183 
AEEc | 1.801061 .5954103 3.02 0.005 .5814186 3.020704 



Post-Estimation Statistics for Regression 

White's test for Ho: homoscedasticity 

against Ha: unrestricted heteroscedasticity 
chi2(5) = 13.69 
Prob>chi2 = 0.0177 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 13.69 5 0.0177 

Skewness | 13.34 2 0.0013 

Kurtosis | 0.12 1 0.7240 

Total | 27.16 8 0.0007 



97 



Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 30 . -243.5664 2 491.1329 493.9353 

The regression output shows an F-score of 42.64 with 30 degrees of freedom; at most, 
73.52% of the variation in the dollar change in total income per capita can be explained by the 
variation in the independent variables. The t-values are all significant. This test passes the "2-? 
Rule of Thumb" as both the HDI and education expenditure t-scores are greater than 2.0 (Gujarati 
& Porter, 2009). The RMSE is 840.87. The White's General Test for Heteroscedasticity reports 
a critical X 2 value of 13.69 which exceeds the X 2 score of 5 degrees of freedom, and which means 
heteroscedasticity exists (p. 387). The IM-test confirms left skewed data at 13.34, and a slightly 
platykurtic at .12. The AIC is 491.1329. The analysis of the equation suggests rejecting the null 
hypothesis, confirming for now that a statistically significant relationship exists. 

As anticipated, this equation yielded a higher degree of "goodness of fit" (p. 386) be- 
tween the variation in the change in income and the variation in the independent variables. 
Testing this equation using the change in the percent of income from the unofficial economy, 
however, will tend to provide a skew in the results that fails to capture magnitude of change that 
is cancelled out due large swings in the opposing variable, and not necessarily a better picture of 
the goodness of fit. Table 4 shows a graphic comparison of these equations. (See Appendix: Ta- 
ble: 4.2) 
Hypothesis 4.3 

The null hypothesis asserts that there is no relationship between the change in Total In- 
come per Capita, AIc T , and two explanatory variables, (1) the change in Education Expenditure 
Dollars per Capita between the EEc pretest and the posttest values, EEci 990 and EEc 2 oo8- Gujarati 
and Porter (2009) explain and support the practice of adding variables to seek higher degrees of 
significance and better over-all fit (pp. 474-475). This equation is a summation of the coeffi- 
cients from the Official Income per Capita and the Unofficial Income per Capita equations. 

98 



Hypothesis 4.3: The variation in the AIc T from 1990 to 2008 is not explained by the vari- 
ation in the HDI iggo the AEEcfrom 1990 to 2008. 
Equation 4. 1 

Null Hypothesis: H : AIc T ^ HDI1990 + AEEC1990.2008 

Maintained Hypothesis: HI: AIc T = HDI 1990 + AEEc 1990 .2oo8 
Summary Statistics 

Variable | Mean Std. Dev. Min Max 

+ 

IcTotalChDc I 1824.1 2225.068 -1382 7862 

HDI1990 I .78 .0575506 .636 .896 

EEChDc I 123.391 266.0138 -272.112 890.705 

Correlation Coefficient 

I HDI1990 IcTota~c EEChDc 

+ 

HDI1990 I 1.0000 
IcTotalChDc I 0.6673 1.0000 

EEChDc I 0.4165 0.4811 1.0000 

Test: Linear Regression 95% Confidence Level 

Regressed dependent variable AIc T using independent variables HDI 199 o, and AEEc 

Source | SS df MS Number of obs = 30 
+ F( 2 , 28) = 18.63 

Model I 138981667 2 69490833.5 Prob > F = 0.0000 
Residual I 104415522 28 3729125.79 R-squared = 0.5710 
+ Ad j R-squared = 0.5404 

Total I 243397189 30 8113239.63 Root MSE = 1931.1 

Alc T I Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 I 1836.063 505.6345 3.63 0.001 800.3174 2871.808 
AEEc I 3.734703 1.367383 2.73 0.011 .9337462 6.535661 

Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 

against Ha: unrestricted heteroscedasticity 
chi2(5) = 11.18 
Prob>chi2 = 0.0479 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 11.18 5 0.0479 
Skewness | 3.15 2 0.2069 
Kurtosis I 0.15 1 0.6965 

+ 

Total I 14.48 8 0.0700 

99 



Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 30 . -268.5085 2 541.0171 543.8195 

The regression output shows an F-score of 18.63 with 30 degrees of freedom; at most, 
54.04% of the variation in the dollar change in total income per capita can be explained by the 
variation in the independent variables. The t- values are significant. This test passes the "2-t Rule 
of Thumb" as both the HD1 and Education Expenditure ?-scores are greater than 2.0 (Gujarati & 
Porter, 2009). The RMSE is 1931.1. The White's General Test for Heteroscedasticity reports a 
critical X 2 value of 11.18 which exceeds the X 2 score of 5 degrees of freedom, and which means 
heteroscedasticity exists (p. 387). The IM-test confirms left skewed data at 3.15/2, and a 
platykurtic at .15/1. The AIC is 541.171. The analysis of the equation suggests rejecting the null 
hypothesis, confirming for now that a statistically significant relationship exists. 

Testing this equation using the change in the total dollars earned, however, will tend to 
provide a skew in the results that captures bigness in the available income, and not necessarily a 
better picture of the equality of its distribution. Table 4.3 shows a graphic comparison of these 
equations. (See Appendix: Table: 4.3) 



100 



CHAPTER 5 
FINDINGS 

This chapter reports the findings of the data analysis and provides a summary of key 
points. Conclusions relative to the research questions follow, and last is a description of some da- 
ta limitations. First, before we review the findings, recall that the object of this thesis is to isolate 
the effect that governance corruption has on the public education budget, as found on the National 
Income Accounting reports as the line item, Education Expenditures as a Percentage of Total 
Government Expenditures (EE). The measure chosen for corruption in governance is that used by 
the International Monetary Fund (Russell, 2010), in what has been referred to as the Shadow 
Economy. All of the results were tested at the 95% confidence level, unless stated otherwise, and 
the results reported below were statistically significant. 

The purpose for Research Question 1 is to set the following foundation. The first step re- 
quired for testing the thesis' sample set of countries against what Kuznets (1934), Sen (1984) and 
others predicted: data using Gross Domestic Product per Capita (Income per Capita, or Ic) do not 
reveal in them the work, education, and training going into the earning of the income, neither 
does it reflect well a measure for a standard of living. 

Working from Research Question 1 , the results of Equation 1 . 1 show a correlation of 
.4599 between the Human Development Index and the change in individual income in the country 
set occupying Central and Eastern Europe. 

The .4599 correlation coefficient is less than .5, the widely accepted benchmark in re- 
search that measures effects of corruption on income (Wong, (2007). This result is consistent 
with expectations, and prompts one to look within the Human Development Index (HDI) to iso- 
late possible relationships among and between the components of the HDI, the Educational 

Attainment Index (EAI), and the Life Expectancy Index (LEI). Notice that this equation purpose- 

101 



fully leaves out the GDP Index (GDPI), to isolate the correlation between just the life expectancy 
and education variables. The correlation coefficient for Equation 1.2 between the change in total 
income and the change in the HDI index components LEI and EAI is very low .0015. These re- 
sults are consistent with Kuznets' (1934, pp. 6-7) work on factors unaccounted for in National 
Income Accounting, Sen's Living Standard (1984), and Sen and Huq's development of the Hu- 
man Development Programme (1990). This second result again prompts a search for another 
explanation for the change in income. 

The next step was to regress the HDI in 1990 (HDI 1990 ) against the change in Ic, to set up 
the proposed theory that the human development earned or attained in a country at the start of the 
test period has significant bearing on individual income. In fact, the linear regression for Equa- 
tion 2.1 showed 40.62% of the variation in change in individual income (AIc ) was explained by 
the variation in the pretest, HDL990, by country. In other words, nearly 60% of the change in 
earning power of the individual, as reported in the official GDP reports, was due to some factor 
other than the country's stock of development in 1990. 

At this point, the Shadow Economy, or the corruption variable, was added to the equa- 
tion. Given that, 40.62% of the variation in the AIc could be attributed to variation in HDL990, 
could we isolate any variation past 40.62% and attribute to corruption? By comparing the R 2 val- 
ues of linear regression 2. 1 to the R 2 value of a linear regression that regressed the AIc 3 against 
two factors: the HDL990 and the Shadow Economy in 2008, linear regression 2.2. Linear regres- 
sion 2.2 showed that 48.44% of the variation in the AIc could be attributed to the variation in 
both the HDI1990 and the Shadow Economy in 2008. 

A comparison of the R 2 shows that adding the corruption variable to the human develop- 
ment variable per country provides a more robust explanation for the AIc per country than does 
the HDL990 alone - from 40.62% to 48.44%. In addition, the data is less skewed. The F-score in 
the second equation is lower, yet still very high, which explains that the shape of the distribution 

102 



is flatter. The increased goodness of fit between individual income and human development 
alone to human development with the shadow economy suggests that underground activity affects 
the country's ability to govern in the best interest of the whole. These findings are consistent 
with works by F. G. Schneider & Enste (2000) on shadow economies, Sen (1997) on human ca- 
pability, and Simon, (1997) on bounded rationality, among others. 

Research Question 3, using Equation 3, adds the variable for Education Expenditure, test- 
ing along with the Shadow Economy, SE. A linear regression regressed EE against the Shadow 
Economy in 2008. The result showed that 19.94% of the variation in change in Education Ex- 
penditure was explained by the variation in the Shadow Economy as a % of GDP in 2008 
(SE 2 oo8)- The high f-score of -2.63 and 98.6% level of confidence suggests that there is a strong 
negative relationship between the level of Shadow Economy in the country and the amount of the 
actual Education Expenditure as a percentage of the total GDP, per capita. This result points to 
the possibility that the Shadow Economy's negative effect may operate, at least in part, through 
the education function in a country, consistent with Mauro (1998), Tanzi (1998), Pritchett (2001) 
and others. 

Since the corruption variable seems to have affected both the change in Education Ex- 
penditure and in individual income in this analysis, it would further the work on New Growth 
Theory to show evidence that a test of the hypotheses in this thesis affirmed its aim. The idea is 
to isolate the change in individual income from 1990 to 2008 due to the Shadow Economy and its 
ability to siphon assets from public funds, and in particular, the funds going into the budget for 
publicly funded education. This point, the motive may need further review. Recall the following. 

Freire wrote, ". . .it would indeed be naive to expect the oppressor elites to carry out a lib- 
erating education" (1970, p. 135). UNESCO reports that, "between 10 and 87 percent of non- 
wage spending on primary education is lost" to "resource leakages" in the execution of the budget 
(Hallak &Poisson, 2007, p. 105). Mauro asserts, "education turns out to be the only component 

103 



of public spending that remains significantly associated with corruption when the level of per 
capita income in 1980 is used as an additional explanatory [control] variable" (1997, p. 10). 

Second, the playing field of education funding is rife with opportunity, in some ways 
greater opportunity than the budgets for other public goods, according to Mauro et al., 2002. 
Rent seeking and state capture by the Shadow Economy are particularly harmful to the allocation 
of education funds (S. Gupta et al., 2000; Mauro et al., 2002). Under-invoicing, collecting tax for 
an un-provided scholastic good service, adding user fees, siphoning funds for text-books or mate- 
rials, and vendor kick-backs are just a few cited examples of the strategies played to abscond with 
public funds dedicated for education (Chua, 2006; S. Gupta et al., 2000, pp. 6-9). 

Third, education may enjoy increasing returns (society gets back more than the dollars it 
put in). Stated otherwise, the funds invested on education, efficiently and effectively, show signs 
of positive externalities (e.g., knowledge spillovers, technology diffusion and adoption, learning 
organizations, specialization and division of labor, and learning by doing). Figure 5.1 below 
shows the relationship between the Education Expenditure as a percentage of total government 
expenditure from 1990 to 2008, on average per citizen, and the Change in Total Income per Capi- 
ta, on average from 1990 to 2007. The two are correlated at .9013, where the greater the 
education expenditures the greater the positive change in total income, on average. These results 
are consistent with other studies on cross-country economic growth and determinants of econom- 
ic development (Barro, 2001b; Barro & Lee, 2001; Romer, 1986; UNECSO, 1998). 



104 



Change in Total Income Per Capita vs 
Education Expenditures per Capita in 2008 




USSR 
' Eastern Bloc, non USSR 
~~ Fitted values 
Correlation Coefficient = .901 3 



500 1000 1500 2000 2500 

Education Expenditures in 2008 stated in Dollars per Capita 



Figure 5.1 Change in Income per Capita and the Education Expenditure. 

Based on the percentage of total government expenditure from 2000 to 2008 



Change in Total Income Per Capita vs 
Education Expenditures per Capita in 1990 



15000- 








■ 


10000- 








■ j, 














■ 








A 


A , 




A 




A 




U 
A ■ 










■ Former USSR 




jV^ 








4 Eastern Bloc, non USSR 










Fitted values 


o- 


*T a 








Correlation Coefficient = .8667 


■ A 
* 















500 1000 1500 2000 2500 

Education Expenditures in 1990 stated in Dollars per Capita 



Figure 5.2 Change in Income per Capita and Lagged Education Expenditure. 

Based on the percentage of total government expenditure from 1990 to 1999 



105 



In the final stage of the analysis, we test aspects of New Growth Theory, starting with 
AIc (Official GDP/Capita, or Ic) on the left side of the equation. Then, using the HDI 1990 as the 
pretest variable on the right side of the equation, we added to it change in Education Expenditure 
(EE); ceteris paribus, the analysis tested three different applications of change in the EE variable. 
The first application was in equation 4. 1 , where the change in Official Income per Capita, (Ico) is 
the dependent variable. The second application was in equation 4.2, where the dependent varia- 
ble was the Unofficial Income per Capita (Icu). The third application was in equation 4.3, which 
the dependent variable was Total Income per Capita, (Ic T ). The reader will find a complete re- 
view of the results on the equation comparison chart on the Table 4.0 in the Appendix. 

Hypothesis 4 tests the effects of governance corruption on education budgets and income 
for the countries in Central and Eastern Europe. The prior analyses show that where corruption is 
a higher, two findings are clear. First from Hypothesis 2, as more of a country's transactions are 
unofficial, On the Ground, rather than official, On the Books, the lower the change in income per 
person since the fall of the Berlin Wall (the HDI 1990 variable equalizes the starting position of 
each country). Second, as more of the productivity moves through unofficial than official chan- 
nels, less is budgeted for education as a percentage of government spending (Hypothesis 3). This 
finding is consistent with by Mauro (1998), Tanzi (1998), Pritchett (2001) and others. Last, Hy- 
pothesis 4 combines the effects of corruption and education spending on individual income. 

The correlation coefficient between the change in Official Income per Capita and the 
HDI in 1990 is 0.7260, with the change in education expenditure is 0.7763, and with the Shadow 
Economy is 0.8882. The correlation coefficient between the change in Unofficial Income per 
Capita and the HDI in 1990 is 0.7994, with the change in education expenditure is 0.7129. The 
correlation coefficient between the change in Total Income per Capita and the HDI in 1990 is 
0.7359, with the change in education expenditure is 0.7875, and the Shadow Economy is already 
accounted for in the total income. 

106 



Variable | 



IcO IcO IcT HDI1990 



EEA$c 



IcO | 

IcU | 

IcT | 

HDI1990 | 

EEA$c | 



1 .0000 
0.8882 
.9971 
.7260 
0.7763 



1.0000 
0. 9070 
0.7994 
0.7129 



1 .0000 

0.7359 1.0000 

0.7875 0.5704 



1.0000 



The linear regression results find a clear relationship between (the independent variables) 
higher rates of corruption and lower percentages on education spending, and (dependent variable) 
lower rate of change in the growth of income per capita from 1990 to 2008. 





Hypothesis / Equation 


a 

o 
■a 

> 

is 

O 


> 

<D 

a 

<D 

-a 
'— 
a 
o 
U 


Pi 

■a 
B 

< 


o 


6 
o 
-a 

— 

o 

u 

o> 

6JJ 

u 

Q 

a 

o 
H 


1 

3 
H 

o 


a 

_u 
'u 

o 
U 

I 
o 

in 
'a 
asi 


U 

1 
o 


s 

2 


& 

_o 

on 

eg 

a> 

LI 
O 

X 
I § 




O 

K 


pa 

1 

o 

g „ 

H ° 
(D O 


on 

I i 

H § 

C O 
O D. 

8 B 

U -a 


a 
.o 

"5 
t3 
Si 
a. 
k. 
«g 

53 

•a 

o 

s 

53 
6 

a> 


H3 

1 
2 

> 

O 

53 

m 




Data Analysis 


































4.1 


IcO^f HDI1990 + EEcl990- 2008 


30 


95% 


0.4797 


18.88 


30 


Y 


Y 


Y 


1493.2 


14.36/5 


3.12/2 


.34/1 


525.5 


17.83/8 


2.229 




4.2 


Icl# HDI1990 + EEc 1990- 2008 


30 


95% 


0.7352 


42.64 


30 


Y 


Y 


Y 


840.87 


13.69/5 


13.34/2 


.12/1 


491.1 


27.16/8 


3.395 


X 


4.3 


IcTV HDI1990 + EEc 1990- 2008 


30 


95% 


0.5404 


18.63 


30 


Y 


Y 


Y 


1931 


11.18/5 


10.37/3 


.96/1 


646.4 


14.48/8 


1.535 




Tab 


e 4.0 Equation 4 C 


^on 


ipar 


ison. 





























Comparing the test results for Research Question 4 by equation highlights several im- 
portant statistics relative to the Economic Horsepower of an economy. Consistent with Maruo 
(1997, 1998, 2000), the public expenditure on public education suffers in the presents of corrup- 
tion. Consistent with findings in several major articles on economic growth (S. Gupta et al., 
1998; Romer, 1994b, 1996, 1998a; Solow, 1956), education funding predicts the variation in cur- 
rent income (31.35 %) with 99% level of confidence. Past education funding predicted 16.64% 
of current individual income (lagging the 1990-1998 education funding average off of the 2008 
income). The combined weight of corruption in the governance function on tax revenues, gener- 
ally, and specifically on the education budget suppresses growth in aggregate and individual 
income. Worse, however, is the long run effect of this corruption on human development, in- 
come, and social capital over time. Since knowledge externalities have a unique ability to 



107 



flourish and produce a compounding effect on economic growth (Cortright, 2001a), suppressing 
funds to the education plant is specifically harmful to technology and innovation diffusion and 
adoption (Klingner & Sabet, 2005). 

This evidence is generalizable to the population of countries, and provides policy makers 
data on the effects of governance corruption on individual income, not available heretofore. The 
analysis shows a highly significant relationship between increased corruption and decreased in- 
come, even after accounting for the income earned, and not counted in the National Income 
Accounting books - earned On the ground. In order for development policy makers to create sus- 
tainable economic growth, corruption at all levels of state, non-state, and private sectors must be 
minimized. Minimizing corruption in government and political corruption, what Moody-Stuart 
calls Grand Corruption (Moody-Stuart, 1996), or what Lowi calls "Big C" corruption (deLeon, 
1993), may help but only to the extent that it is not somehow networked with petty, "Little c" or 
private sector corruption. For the purposes of this thesis, it is more important to categorize the 
corruption by its effect on social capital, on tax revenue, human development, education funding, 
or other items that may be more or less measureable. 



108 



Summary 

This thesis addresses economic development policy so as to encourage healthy economic 
growth (Kuznets, 1966, p. 493) without the friction of institutional corruption; to inform devel- 
opment policy toward a more balanced growth, by offering policy makers a unique method of 
measuring governance corruption's effects on education budgets and individual income. New 
Growth Theory is the foundation for this work. Maddison (2009) provided evidence that econo- 
mies, for over two thousand years, have grown. Maddison's data show that since 1820, the 
average yearly world GDP growth is 2.21 percent (2009, p. 4). Further, he showed that the pace 
of growth increased approximately at the time of the industrial revolutions in Europe and North 
America. The IMF is projecting a 4% global growth for 201 1 and 2012, with advanced econo- 
mies growing 1.5% to 2% (UPI, 201 1, p. 1). Other evidence a shows "that poverty and inequality 
are on the rise" (Lozada, 2002, p. 5), leaving some to believe that a direct and positive correlation 
between growth and increased poverty and inequality exists. It does not necessarily follow, how- 
ever, that the growth is responsible for increasing poverty or widening the gap of inequality 
(Gujarati & Porter, 2009). Yet, in some popular media and political circles, economic growth 
carries with it a stigma - poverty worsens in the wake of economic progress. 

The distinction between correlation and causation in this debate is critical. Policy advi- 
sors must make the distinction when forming policy decisions regarding economic development 
in light of reports that seem to link the two. This thesis asserts that linking economic growth di- 
rectly to individual income as a measure of individual prosperity is too simplistic, and that 
governance corruption erodes, among other public goods, education. Further, it asserts that cer- 
tain factors of history, governance, and education effect individual income. 

Recall the four conceptual challenges, which concern inconsistent definitions and meas- 
urement methods for (1) economic growth, (2) the period analyzed, (3) living standards, (4) 
corruption. Also, recall the three problem themes. (1) There are gaps in the literature specifically 

109 



tying corruption to a mechanism that reduces Income per Capita. (2) Measuring governance, cor- 
ruption in governance, (aggregate) economic development, and individual income (Income per 
Capita) levels is difficult. (3) The scope of this thesis necessarily excludes important variables, 
which serve as a launching point for future research. 

The key hypothesis this thesis tested and maintained is: governance corruption's effects 
on education through the public resource mechanism (Government Expenditure on Public Educa- 
tion as a percentage of Total Government Expenditures) are direct and negative; the higher the 
degree of corruption, the lower the relative education budget per capita. Further, the lower the 
education budget per capita, the lower the relative individual income. 

In Governance Matters (Kaufmann et al., 2008), authors writing on behalf of the World 
Bank explain the criticality of good governance, sound public administration, and effective public 
policy. The World Bank embarked on the massive project to understand matters of governance in 
the early 1990s, in order define its dimensions and measure its effectiveness for future genera- 
tions; understanding governance to measure it became an international quest. The IMF, World 
Bank, UN agencies, and other IGOs set a course toward understanding the factors that encourage 
and that impeding good governance. "Tackling the issue of measuring governance was the prem- 
ise of a meeting of scholars, data experts, clients, donors, and policy makers at the Kennedy 
School of Government, Harvard University, in May 2003" (Besancon, 2003, p. 1). Sustainable 
human and economic development depends on good governance (Sen, 1999). Good governance 
supports sound public policy, transparent governmental operations, and its fiscal discipline; pub- 
lic expenditures on public goods, including that which is spent on public education, depend on 
good governance (S. Gupta et al., 2000). 

Knowledge about and literature on the links between governance and corruption are not 
new. Quoting Suetonius (1 10, p. 82), "Et tu, Brute?" However the literature on links between 
good governance and less corruption, between weak governance and high levels of corruption, 

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has become increasingly more frequently published in public policy literature since the 1970s 
(Rose-Ackerman, 1978, 1999; Abed et al., 2002). The vast body of literature on corruption is in- 
extricable from that of governance, yet yields its own avenues of study. However, grand or petty, 
political or private sector, rewarded by profit or revenge, malicious or beneficial to the engine of 
development, governance corruption reduces tax revenue - the government's income stream - 
and is therefore, detrimental to the budget available to fund public education (deLeon, 1993; 
Pritchett, 2001; Heidenheimer et al., 2002; Johnston, 2005). 

Measuring the effects of corruption and specifically its reduction of a government's fiscal 
budget, is a discipline aided by the advances in technology and intercommunication between 
counties in the flattening world (T. L. Friedman, 2005), and it is crucial if policy makers are to 
create and protect budgets with sound evidence that the budgets may be at risk, and how. Several 
studies have produced data on the size of the Shadow Economy as a gauge for the degree of cor- 
ruption in a country. The studies use shared or similar data, and similar (or different) methods to 
isolate that productivity which goes unreported to the government (Tanzi, 1998; DellAnno et al., 
2006; La Porta & Shleifer, 2008). While the test results published from these studies report a dif- 
ference in the magnitude of the Shadow Economy, the variance in the results is relatively small, 
and is highly correlated across methods. The results are also highly correlated with published da- 
ta on the level of corruption based on surveys such as the World Bank's Business Environment 
and Enterprise Survey (BEEPS, 2008), and Transparency International's Corruptions Perceptions 
Index (CPI, 2010a). The only study with adequate coverage of Central and Eastern Europe was 
sponsored by the World Bank and published in 2010, Shadow Economies all over the World: New 
Estimates for 162 Countries from 1999 to 2008. In this study, the country with smallest estimat- 
ed Shadow Economy is Switzerland at 8.6%, and the highest is the country of Georgia, at 68.8% 
(Schneider et al., 2010, p. 27). The size of the Shadow Economy (GDPu) is added to the size of 
the formal economy (GDP ) for the total Gross Domestic Product per country (GDP T ). (Each 

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GDP figure is normalized by dividing by population for Income per Capita [Ic , Icy, and Ic T ]). 
This particular treatment of GPD per capita is different from any treatment of the size of the 
Shadow Economy uncovered to date in the available bodies of literature and research. 

The body of literature around income inequality and converging or diverging incomes has 
fueled many a debate in the media and between scholars (Kuznets, 1940). However, the data are 
(at best) misleading without considerable attention paid to context. According to La Porta & 
Shleifer (2008), official GDP is only 91.4% accurate in the best-case scenario, and on average, 
70% accurate. These authors assert "[t]he various estimates thus suggest that, in the average 
country, roughly 30% of the economy is informal" (p. 9). Some researchers assert that in the 
former Soviet countries, corruption is systemic (deLeon & Green, 2004), and accounts for far 
more of the economic activity than studies to date have realized or uncovered (Stefes, 1997). Es- 
timates on inequality can be no more accurate than the data going in, leaving policy makers 
potentially misinformed. In an effort to produce the most accurate account of individual income, 
adding that which is On the Books of National Income Accounting to that which is known to be 
On the Ground is a step toward accuracy in the convergence / divergence debate. By adding the 
two streams of income, the data reveal more about the effects of the Shadow Economy on educa- 
tion expenditures than did the data absent the Shadow Economy productivity - results from 
testing Research Question 3 and Hypothesis 3 tell us that 31.25% of the variation in the change in 
Education Expenditures (EEc) can be explained by the Shadow Economy (SE 2 oosX at 95% level 
of significance, on this sample set. 

New Growth Theory stands apart from other economic growth theories for three reasons. 
First, NGT shoulders change, in that the theory allow for changing returns to scale. Regardless of 
its origin, regardless of its pace, regardless of its variety, NGT is an equal opportunity theory - a 
requisite advantage in a flattening world (Friedman, 2005). Second, models and equations testing 
NGT do not force technical accumulation, or any other variable, to be held constant across the 

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sample set or time (Cortright, 2001). Logically it holds that when adoption is voluntary, if pos- 
sessing the education required to adopt technology precedes its adoption, and gaining knowledge 
from the technical adoption follows its adoption, then education is inextricable from the process 
of technical change. Technology adoption may not be voluntary. For example, analog television 
signals were phased out in the US, and one invention, the radar gun, shaped the art of pitching 
baseballs and catching speeding automobiles. When adoption is not voluntary, its knowledge 
may precede it, may be simultaneous to it, or may lag its arrival, and knowledge gained by it still 
follows - may be at a similar or different pace. 

Pace matters. The pace of adoption and diffusion, the pace of education and knowledge 
gains, and the pace of other factors of economic growth are each important pieces of the growth 
puzzle (Maddison, 2009). NGT acknowledges the individuality by country and its methods and 
models allow for variations in data across countries and over time - aided by advances in compu- 
ting technology and statistical software that allow for the processing of large sets of cross-country 
longitudinal data, such as ST AT A (2007), used to process the data. 

Economies cycle - from trough to peak to trough to peak, etc. . ., measuring a cycle re- 
quires discipline - from trough to trough (Burns & Mitchell, 1946; Kuznets, 1940; Schumpeter, 
1939), and each country has its own rhythm. Analyzing economic data using arbitrary dates or 
dates uncoupled from economic cycles, may yield exaggerated results, too high or too low. An 
example of this common practice would be measuring GDP growth from 1990 to 2000, or worse, 
the same GDP growth across countries. Policy makers would be better informed if economic data 
were reported in light of the cycle position. Economies cycle. Gross Domestic Product per capita 
is an economic factor that follows this rule. GDP per capita cycles, and to inform policy makers 
with consistent and reliable information, the cycle is measured from trough to trough. 

Economies cycle while they grow at an average rate of 2.21% since 1820, and at least 
1.4% since the time of Christ (Maddison, 2009, p. 4). Economies grow. Economies may shrink 

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for a time, they may even feel the effects of a long depression, but on average for over 2,000 
years, they grow. 

Economies oscillate by the rhythm of the business cycle (about 4 years) (Kitchin, 1923; 
Mitchell, 1928). Economies cycle in short waves with the rhythm of between 2 and 5 oscillations 
(about 8 to 20 years) (Juglar, 1893; Rostow, 1975; Schumpeter, 1939). Economies cycle in long 
waves by the rhythm of 3 to 4 short waves (about 35 to 60 years) (Kondratiev, 1926; Rostow, 
1991), and the point in the cycle where a measurement starts or stops is important. Absent infor- 
mation on the phase of economic cycles, it is possible to measure Country A starting at its triple 
trough (the lowest point in a compound business cycle), against Country B starting at its triple 
peak (the highest point in a compound business cycle) - yielding an accurate comparison. Sup- 
pose the ending data recorded the GDP in the opposite phase of the cycle for both countries. 
Further, suppose US policy makers are voting on foreign aid for these two countries. Country 
A's need for aid would likely be underestimated, and Country B's overestimated. Compound this 
scenario by supposing that Country B is the Country of Georgia, where 68.8% of the total GDP is 
in the hidden economy. 

According to Rostow (1991), each country's readiness for growth, such faculties as infra- 
structure and fiscal preparedness, lay a foundation for economies to enter a growth stage. Events 
or political upheaval may trigger a trough (e.g., natural disasters such as Japan's 201 1 earthquake, 
or acts of aggression such as Japan's attack on America's Pearl Harbor). Events or innovations 
may spark long wave growth (e.g., the industrial revolution, or the personal computer). The polit- 
ical upheaval in 1989 was the dissolution of the Soviet Empire, the fall of the Berlin Wall, the 
lifting of the Iron Curtain, the dismantling of USSR's economic and governance infrastructure, 
and the subsequent governance and policy challenges facing re -born states with still open scars of 
the Cold War. Shock waves of change hit the neighboring states, and radiated outward to those 
states bordering the Eastern Bloc. After the political shock, each of the economies suffered. As 

114 



seen in Appendix B, Figure 6, each country experienced at least one trough as measured in In- 
come per Capita. Most countries show evidences of more than one cycle, and some a double -dip 
trough. Many economic factors that affect the GDP and economic cycles of Eastern and Central 
European Countries are outside the scope of this thesis. 

The slice of enormous body of literature on measuring education pertinent to this thesis 
measures the Government Expenditure on Public Education as a Percentage of Total Spending 
(EE) as found in UNESCO's Global Education Digest (2010, Table 13), and the 201 1 World 
Development Indicators statistical database (HDR, 2010f). The process of educating is outside 
the scope of this thesis. The budget figure EE is normalized by dividing by population (EEc). 

Central and Eastern Europe, as a region is "geographically imprecise" (Kornai, 2005), at 
least in part due to the geography, but also due to its changing authorities since the fall of the 
Roman Empire in 1453 (Robinson, 1902, p. 356). It was Winston Churchill, in his Sinews of 
Peace Speech, who effectively drew the map for the land beyond the Iron Curtain. 

From Stettin in the Baltic to Trieste in the Adriatic, an iron curtain had descended across 
the continent. Behind that line lie all the capitals of the ancient states of Central and Eastern Eu- 
rope. Warsaw, Berlin, Prague, Vienna, Budapest, Belgrade, Bucharest, and Sofia, all these 
famous cities and the populations around them lie in what I must call the Soviet Sphere, and all 
are subject in one form or another, not only to Soviet influence, but to a very high and, in some 
cases, increasing measure of control from Moscow (Churchill, 1946). 

'Socialism' is also imprecise. So are shadow, parallel, and unofficial markets by any 
name. It is the effect of Socialism, governance, corruption, and the effect of the Shadow Econo- 
my that must be measured to provide policy makers with the fodder they need to make critical 
development funding decisions. 

Finally, the data provide evidence to inform the development policy debate in several 
critical areas: economic development, fiscal policy, education funding, and corruption in govern- 

115 



ance. The application of the method used to measure total income and education funding is dis- 
tinctive to this thesis, while the findings are consistent with published research Mauro (1998). 
The data show that governance corruption's effects on education through the public resource 
mechanism are direct and negative; the higher the percentage of corruption, the lower the relative 
education budget per capita, affirming prior work by Mauro (1998), Tanzi (1998), Pritchett 
(2001) and others. Further, the higher the percentage of corruption, the lower the relative indi- 
vidual income, affirming New Growth Theory (Romer, 1998b). 



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Data Limitations 

The concepts of governance, corruption, parallel economies, and government accounta- 
bility, are not easily measured nor are they naturally calibrated to each other. For purposes of 
comparison, testing, and regression, a common denominator or an index simplifies the equations 
and helps the researcher make sense out of the test results. This requisite forces the difficult step 
of calibration to come first. In an international endeavor to accommodate the needs of the re- 
search community, the leading international agencies have each launched divisions dedicated to 
statistical capacity building and data collection. The rapid advances in computing technology and 
software has enabled and stimulated quicker analysis, new methods, and more robust testing, ex- 
amples of which follow. However, the added statistical capacity meets with frustration for some 
researchers. 

Governance's most widely accepted barometer is the World Governance Indicators 
(WGI) - most of the data are survey based. The most widely accepted barometers for corruption 
are the Global Corruption Barometer, and the Corruption Perceptions Index, which are both sur- 
vey based. The European Bank for Reconstruction and Development (EBRD) issues the Business 
Environment and Enterprise Performance Survey (BEEPS) every five years. A robust survey of 
over 15,000 professionals, the BEEPS scores offer a broad look at the corruption in the business 
sector, or between business and government. While the BEEPS would inform the rent seeking 
reports, it does not measure the rent seeking in a dollar figure. The Shadow Economy is estimat- 
ed by the Bribe Payer's Index, Economic Freedom Index, and by various gap analyses such as 
electricity demand, consumption demand, arbitrage, and tax gaps. These gaps purport to measure 
that which is missing from or avoids the official economy. These methods presume to measure 
corruption's effect on the economy by back filling - that is, by filling in the gaps in information 
found by auditing. 



117 



Research is underway to codify factors of governance, corruption, and transactions miss- 
ing on official registers. Today, however, each of the estimating methods lacks precision, so do 
analyses based on them. From the UN's Millennial Development Project to researchers on the 
ground, creating better measures and methods for estimating the efficiency and effectiveness of 
government is a top priority. Comparing this author's first experience collecting data on income 
inequality and government effectiveness in 2005, to the experience collecting the same data today 
is a challenge; very little about the experiences are the same. Following are three disciplines with 
significant overlap that may inform future research. 

(1) Technology improvements in hardware, software, and computing capability. 

(2) Digital communications, and digital library resource catalogs, and resource availability. 

(3) Increased collaboration between international agencies, data availability, and data consisten- 
cy. In six years, the change is extraordinary in every way, which serves as the basis for this first 
caveat. Another year would likely produce more robust results and additional insights. For now, 
the most objective method to estimate corruption's effects on education expenditures and individ- 
ual income in the Eastern and Central Europe is the MIMIC method on the Shadow Economy 
data produced by Schneider et al. (2010). 

Next, UNICEF was the agency responsible for collecting data on education expenditures 
by country every five years from 1965 until 1988. Submissions of national accounts data on edu- 
cation were voluntary and inconsistent. UNESCO undertook the task of standardizing education 
statistical data and its collection, collecting its first round of data in 1998. Data representing edu- 
cation expenditures as a percentage of total government expenditures is limit until 1988, scant 
from 1988 to 1998, and nearly 100% from 1998 to 2008 on the sample of countries in this thesis. 
The lack of data availability for this project's needs and for similar projects by many other re- 
searchers in similar want of good education data is the basis for this second caveat. Based on this 
author's research, none of the agencies has plans to backfill the government expenditure data. 

118 



Therefore, the acquisition of additional or more robust measurements of education funding will 
remain elusive, unless the education data may become apparent through backfilling. 

The global push by the international agencies for better data and statistical capability 
through the implementation of international accounting standards is underway at the IMF and 
World Bank. This step will provide data to complete country-to-country gap analyses. The aug- 
mented data can feed consistent methodologies, primary governance research on the ground, and 
many other initiatives, which will likely induce extraordinary change in the next six years as it 
did in the past six. 

The Shadow Economy as measured by the MIMIC method is one of many methods used 
in recent research to estimate the scope or size of the unofficial economy in a country. While the 
methods are highly correlated, each has limitations, and the Shadow Economy may overstate or 
understate the actual unofficial economy. Prudence may suggest that policy analysts employ 
more than one measure and method to estimate the extent of the unofficial economy in a given 
country. 

Thirdly, the method used to test governance corruption's effects on education budgets 
and income is generalizable and scalable to the balance of the 194 sovereign countries recognized 
in the world today. Analyzing the balance of the countries may be beneficial to policy makers in 
many ways. While data advance, measuring the effects of governance where data is available 
may highlight new results or different relationships. The sample set of countries analyzed herein 
represent all of the former USSR (15 countries in Group 1) and its satellite states (15 countries as 
of 2008 in Group 2). However, a third group of only six countries in Central Europe fall into or 
could fall under the rules set out to isolate Soviet influence. (See Appendix: Country Briefs, 
Group 3). Recall the following is the list of rules for inclusion in the Sample Set of Countries. 



119 



Rule 1 : The country was or remains Socialist 

Rule 2: Four or more years of Soviet influence (Sachs & Warner, 1992, 1996, 1998), plus a cre- 
ated, liberated, or re-gained sovereignty, independence or the ability to trade, travel, and migrate 
which began between 1988 and 1992. 

Rule 3: Geographically related by inland border, trade route, or sea-trade route 
Rule 4: Ethnolinguistically interrelated, Economically interdependent 

Using different rules or relaxing these rules, including additional countries in Europe to 
increase the sample size, or analyzing different country groups based on these or other criteria 
may yield different findings. For example, the OECD nations, the balance of Europe, and many 
other countries have data on the Education Expenditure and Shadow Economy variables. Analyz- 
ing these additional countries may expose findings specific to the Central and Eastern European 
data set that are due to the particular countries in the set. Exposing findings peculiar to the data 
set used in this thesis necessarily requires comparison against other sets of countries. 

A fourth and a major limitation to this data is its narrow scope. Many variables that are 
widely used in cross-country analysis on economic growth and individual income in transition 
countries are beyond the scope of this thesis. Specifically, future research on this sample set 
would include three important variables. (1) A variable critical to economic productivity would 
measure progress toward market liberalization. In the 2010 Transition Report, the EBRD offers a 
"new sector-based approach to measuring transition progress," which provides data on privatiza- 
tion, markets, banking, and infrastructure (p. 3). (2) Progress toward EU accession, measured by 
the European Commission (201 lb). (3) A variable critical to understanding economic growth 
patterns would mark the history and intensity of armed conflicts (HIIK, 2010a). 

Other variables that are nonspecific to transition countries are left for future research. 
Specifically missing are (1) the variables for public expenditure on public goods other than edu- 
cation, and the policies that surround those goods, (2) variables that measure the education plant 

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after funds are appropriated and through performance; the area this thesis avoids, treating 'educa- 
tion' as a black box. (3) In addition to the public goods variables, the remaining dimensions of 
governance (Kaufmann et al., 2008) interplay with education and its funding. Isolating education 
is as fragile as the ceteris paribus assumption - that all else remains unchanged - is permanent. 

Fifth, the elementary level of econometrics performed on models may leave inside much 
of the information possible to extract from this data, would an econometrician be at the controls. 
Further, the more robust tests, better analysis or more intricate modeling may uncover different 
findings that support either better or worse, the hypothesis herein. 

Lastly, and possibly most importantly, the data do not have in them the ability to predict 
beyond the forecasting, which is based on imperfect empirical data and extrapolation, and in- 
clined to human error. The ceteris paribus assumption creates risk for the policy analyst, and 
builds error into any equation. In turn, the policy makers are at risk of ill-informed policy. 



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CHAPTER 6 
CONSLUSIONS AND FUTURE RESEARCH 

Conclusions 

The analyses presented above suggest clear evidence that as the size of the Shadow 
Economy increases, the budget for Education Expenditures as a percentage of the total govern- 
ment expenses decreases. There is also evidence that as the Shadow Economy increases and 
Education Expenditures decrease, individual income decreases as well. These findings are con- 
sistent with New Growth Theory, which posits that the quality and quantity of education 
specifically, provided as a public good, is critical to a healthy and sustainable economic develop- 
ment. The practical application of this evidence is different, in that education expenditures and 
individual income are analyzed together and in light of the effect of corruption on them. This 
process requires that we compare the results of three equations: (1) Official Income per Capita, 
(2) Unofficial Income per Capita, and (3) Total Income per Capita. 

Minimizing corruption, the ideas, case studies, and methods form a large body of litera- 
ture, much of which is outside the scope of this thesis, but the motivation for policy makers to 
minimize corruption may be greater in light of this new analysis. Two points with the potential to 
affect the policy agenda deserve repeating. (1) Data do not support as a sustainable solution the 
allowing of petty corruption as a means to grease the wheel of the economy (Gupta et al., 2000, p. 
9). (2) Corruption is still corrupt, and there is no evidence in this analysis that small-scale, or 
Grande, black, white, or grey, Big "C" or little "c," that corruption pays the average individual 
well over the long term (Rose-Ackerman, 1999a, pp. 16, 26). Eventually, society corrodes, 
school quality decays, infrastructure suffers, budgets for research and development shrink, and 
the economy implodes onto itself, not unlike that described in Levy (2007) since the end of the 

122 



Cold War, and not unlike the implosion of the Soviet Union itself. The IMF describes the process 
in The IMF and Good Governance (201 Id). 

Corruption can reduce investment and economic growth; divert public resources to pri- 
vate gains and away from needed public spending on education and health. ... By reducing tax 
revenue, corruption can complicate macroeconomic management, and since it tends to do so in a 
regressive way, it can accentuate income inequality (IMF, 201 Id, p. 1). DeLeon asserts that rid- 
ding a society of its corruption is unlikely, as "[i]t is sown in Corruption" (quoting 1 Corinthians 
15:42, 1993, p. 3). While petty or little 'c' corruption may 'sown in' to the fabric of cultures, in- 
formed policies may dissuade some of it. To do so, Rose-Ackerman (1999a, p. 4) suggests 
reforms in governance, especially in the rule of law. 

Reforms can reduce the incentives for bribery and increase the risks of corruption. The 
goal is not to eliminate corruption, but to improve the overall efficiency, fairness, and legitimacy 
of the state. Hopes for total elimination of corruption will never be worthwhile, but steps can be 
taken to limit its reach and reduce the harm it causes. 

Focus on policies that promote vertical and horizontal accountability in both state and 
non-state institutions may dissuade corruption. These may include policies intended toward 
whistle-blower protection, incentives, enforceable penalties, and more significant fines (Relly, 
2011, p. 5; Rose-Ackerman, 2008). 

A summary of the applicability of governance corruption in the process of education pol- 
icy making, cites four major ways in which corruption targets public budgets; through rent 
seeking, state capture, control, and bid-rigging. Education may be an attractive budget to target, 
as the effects may be hard to detect, or help to realize a private or cooperative motivation. 

(l)Rent seeking that is easy to hide from public view. Education will likely continue 
with old books, buildings, and technology, thus, its funding is easier to reduce without much 



123 



more attention than, say, a bridge. Actions such as this are rent-seeking, "leaving the education 
sector under-resourced" (TI, 2009b, p. 2). 

(2)State capture, in the form of collusion, that is difficult to identify and even harder to 
trace to its source. Rose-Ackerman (1999a, pp. 26, 32) asserts that education facilities will likely 
remain without new desks, thus, its funding is easy to divert to provide remuneration for a corrupt 
act. 

(3)Control over the masses by controlling the quality, quantity, or content of education. 
One method may be censorship. Some scholars assert the possibility that education of the general 
population is not in the best interest of a corrupt ruling party. A corrupt individual or powerful 
institution may advance the type of education that furthers a specific agenda (Freire, 1970, pp. 55, 
81, 135). 

(4)Bid-rigging (no-bid or sole source contracts, contract favoritism) assures that contracts 
are awarded to a particular supplier or firm and clientelism, cronyism, patronage, or nepotism 
may sway contract decisions (Rose-Ackerman, 1999a, p. 27). According to Mauro (2002, p 278), 
"[e]ducation stands out as a particularly unattractive [rigging] target" for two reasons, (a) Con- 
tracts for school supplies are small-scale relative to defense or transportation contracts, (b) 
School systems need relatively low technology while rigging requires "widely available, mature 
technology" (p. 277). Allocating funds away from the education sector and toward, for example, 
defense contracts, would be one way to manipulate public budgets. The "direction of the causal 
link is at least in part from corruption to the composition of spending" (p. 277). 

Education's positive externality is questioned. Despite the assertions of many researchers 
(e.g., Romer, 1986, p. 40; Arrow, 1962; Solow, 1957) that education "spills over" to other learn- 
ers and to other disciplines, and therefore, has an increasing return on investment, the evidence is 
mixed. The question becomes, how or why does the money spent on education not spillover, take 
root, and bloom into new knowledge and a more educated society? If, in fact, the money is 

124 



reaching the level of the student, or to the extent that it does, what is happening to the value edu- 
cation passes on? Does it evaporate? The answer may be in accordance with Ofer (1997), that 
the unofficial economy is realizing the gains. Another debate is whether knowledge spillovers 
mete out different results, since knowledge is owned by the individual actor rather while educa- 
tion is merely available and to a questionable degree. 

However well intended the Constitution of the Soviet Union, however well intended the 
leaders, human development and a broadness of education, among other factors, suffered in the 
hands of the Soviet Empire. Moldova, Tajikistan, Kyrgyzstan, Ukraine, and Serbia have yet to 
realize the GDP per capita known to each country prior to the dissolution of the USSR. Certain- 
ly, other factors are involved, but of those, are any very far removed from corruption? The 
average Shadow Economy in these five countries is over 50%. The average GDP per capita $762 
per year and including that gained in on the ground is $939 (stated in US 2000 dollars) (Schneider 
et al., 2010). These GDP per capita levels are similar to those found in the lowest earning regions 
of Africa. It seems illogical that a corrupt individual would willingly advance the type of educa- 
tion that others could use to unseat him or her, as a matter of simple self-preservation (Freire, 
1970, pp. 55, 81, 135); Monas (1984) provides the following account. 

Institutional censorship plays an especially harsh, continuous, wide, and deep 
role in Russia. ... The related "sacral aura" or sacred pretension that surrounded 
that power had to be protected from irreverent attacks or underminings, in the in- 
terests of stability. Of course, the interests of stability are generally the interests 
of the ruling class, of the 'fathers', the patricians (p. 166. emphasis in original). 

Systemic, pervasive corruption does not pay off for the average individual in the long 
run. Research is ongoing by the IMF and the World Bank to separate or isolate the effects of 
governance corruption in the funding of different public goods, efforts that may inform hypothe- 
sis in this thesis. 

Economies cycle; economies oscillate from trough to peak to trough to peak. Measuring 
a cycle requires discipline. GDP cycles start at the bottom of a trough and end at the bottom of 

125 



the next trough (Burns & Mitchell, 1946; Kuznets, 1940; Schumpeter, 1939), and each country 
has its own rhythm. Gross Domestic Product per Capita is an economic factor that follows this 
rule. GDP per Capita cycles. Encouraging policy analysis to weigh the economic cycle when 
producing formulas, models, and equations is responsible, intelligent analysis. 

This thesis provides a strong case for transparency in National Income Accounting. As 
more countries embrace transparency, more countries that share data may uncover more evidence 
of the Shadow Economy's ability to avoid detection. Backfilling the missing information availa- 
ble on one side of the transaction will take time and research, work that is ongoing through 
international agencies such as Transparency International and the IMF. Increasing sanctions for 
lack of transparency are increasing, as well. 

Sustainable development depends on many factors, one of which is an educated populace, 
who are capable of adopting the technology that drives economies to a new steady state (Schum- 
peter, 1939). Incentive to produce within the governance system must outweigh the incentives to 
produce Off the books. As Tanzi (1998) noted, there is both a supply of and a demand for corrup- 
tion. As incentives to engage in corruption decrease, as the remuneration dries up, the demand to 
engage in corrupt behaviors should decrease. Proper audits, oversight, policies, law, and conse- 
quences may reduce incentives to supply or allow the means or the remuneration for corruption. 

The next step for research in this area is to provide policy makers evidence to support 
protecting the education budgets, specifically. Milton Friedman (1997), arguing against publicly 
funded education, foresaw this education funding dilemma, asserting very generically that where 
the public money goes, so goes the corruption. Possible research areas would be methods of 
funding for education that protect the funding stream, such as conditional block grants or match- 
ing grants. Research in this vein would assist in the education policy planning and education 
budgeting stages. 



126 



Another vein for future research would inform policy implementation. If corrupt institu- 
tions are part of the problem in the delivery of the education product, then an international 
agency, consultant, or a funding source such as US AID that will provide funding conditional on 
also providing the policy planning and work force to implement it (deLeon & Green, 2002) may 
best handle implementation. Research on implementation strategies may inform policy makers 
about approaches that protect the funding stream, and ensure that funds progress to the level of 
the schools, or better, to the level of the student. If the funds are secured to the level of the 
school, if education is made available and the availability is monitored by outside agencies, new 
data may be collected to further knowledge about the value of education on human development 
and economic growth. It is likely, however, that changing the funding mechanism and level 
would bring about its own challenges, as corruption follows the available reward to its new 
source. 

Susan Rose-Ackerman (1999) suggested an international tribunal could govern interna- 
tional corruption issues. However, if corruption is "sown in" to the fabric of our being and 
therefore of our society, the budget for thwarting it globally may be out of reach (deLeon, 1993, p. 
3. quoting 1 Corinthians 15:42). Accepting that some corruption has and will always be part of the 
polity, theoretically, suggests the need for a sieve through which petty corruption passes, expos- 
ing the bigger crimes. This would necessitate a rule of law that invites and protects whistle 
blowing. 

Protecting education budgets from state capture, rent seeking, and bid-rigging starts with 
acknowledging that education budgets are susceptible - even likely - targets. Further, protection 
must continue through the policy process. Several suggestions for this protection are found in 
Lester Salamon's (2002) Tools of Government, and Rose-Ackerman' s Corruption in Government 
(2008, p. 340). 



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Future Research 

The first priority in future research is to pick up the analysis of the Shadow Economy's 
effects on education budgets and individual income, where it left off. Many studies on economic 
growth use cross-country analyses, and many use conditioning and dummy variables with robust 
econometrics. Transition country studies, in particular, generally employ three important varia- 
bles: (1) Progress toward market liberalization. In the 2010 Transition Report, the EBRD offers 
a "new sector-based approach to measuring transition progress," which provides data on privati- 
zation, markets, banking, and infrastructure (p. 3). (2) Progress toward EU accession, measured 
by the European Commission (201 lb). (3) Accounting for the history and intensity of armed con- 
flicts (HIIK, 2010a). 

Another future research project would include additional variables that are nonspecific to 
transition countries, but are common to cross-country regression analysis of time-series data. A 
partial list of these variables would include data on: (1) Public expenditure on public goods and 
infrastructure (in addition to education). (2) The policy implications central to the budgeting and 
delivery of those goods. (3) Education funding research that isolates effectiveness and efficiency 
in the education plant. (4) The remaining dimensions of governance (in addition to public goods) 
(Kaufmann et al., 2008). (5) Interplay between education, and other infrastructure or public 
good funding and the composition of government revenues. 

The method used to test governance corruption's effects on education budgets and in- 
come is generalizable and scalable to the balance of the 194 sovereign countries recognized in the 
world today. Analyzing the balance of the countries may be beneficial to policy makers in many 
ways. While data advance, measuring the effects of governance where data are available may 
highlight new results or different relationships. The countries bordering the Eastern Bloc, along 
the Iron Curtain, are likely to be effected by its creation and demolition, (Diamond, 1997; 
Elisseeff, 1997) (See Appendix: Data Validity and Reliability). Then, the countries with regional 

128 



trading ties and ethnolinguistic similarities would make another logical grouping, followed by 
adding the Western European countries to the sample set. Rather than geographical groupings, 
one might sort the 194 sovereign countries based on many factors, including the number of years 
of governance stability, Communist rule, centralized planning, democratic rule, or armed conflict. 

Kuznets' economic paradigm was the genesis of this thesis, which can be translated to a 
Kuznets Curve for education, not unlike the Kuznetsian curve for the environment (Stern, 2003). 
His original curve contrasted Income per Capita against Income Inequality over time, where 
'time' started with a less mature state characterized by a more dispersed and agrarian population, 
moving toward a population concentrated near industrial centers and a more specialized work- 
force population. The Kuznets curve and the cycle that societies move through, a Kuznets Cycle 
(Kuznets, 1934, 1940, 1966) are represented in Figure 6.2. The basic Kuznets Curve shows in- 
come inequality increasing and the decreasing over the cycle. 




Figure 6.2 The Kuznets Curve (1966). 



Applying the general principles of the original and the environmental Kuznets curve to a 
model similar to that found in equations 4. 1-4-3 in this thesis may yield important policy infor- 
mation for analysis and administrators alike. This model would graph education funding over the 

129 



maturation cycle of a governance regime With information on the effects of the Shadow Econo- 
my on education funding, one could employ a MIMIC equation used in Schneider et al. (2010) to 
create the Educational Kuznets Curve seen in Figure 6.3, which shows the effects of governance 
corruption on education funding in the instance of a newly independent state. 

Educational Kuznets Curve 

i 

o 

c 
o 
o 
LU 

o =■= 
T3 
CO 



CO 
>> 



CD 
> 

CO 

a: 



CD 



% 

(J) o 



CD 



CO 

o 



New State 



Education Funding 





Mature Governance 



Low 



Time 
Education Expenditure Policy Development 



High 



Figure 6.3 Educational Kuznets Curve (author's depiction). 

The Educational Kuznets Curve shows a divergence in the funding of education as a 
country from when it embarks on a path toward a on a campaign toward good governance. Dur- 
ing this period, and relative to the degree of good governance, the hidden economy prospers. As 
governance in the new state matures, and as the rule of law becomes more enforceable, as devel- 
opment policy matures, more productivity moves into the official economy (Kornai, 2005; 

Mauro, 2004a). We can assume this movement of productivity to the official economy increases 

130 



available funds for public goods, including education spending. The education inequality would 
decrease over the latter half of new state governance implementation. 

This idea has important implications in development and sustainability. Rather than a 
new state falling victim to a full cycle of governance maturity, proactive development planning 
could arrest the cycle, or prevent it. Following is the sequence of logic. Mauro (1998), Barro 
(2001), and Solow (1956), among others, assert that education is fundamental to economic 
growth. The correlation coefficient between EE (lagged variable that averages from t-10 to t-18 
years) and Ale is .8667. According to Mauro (1998, 2000, 2002), Tanzi (1998), and Pritchett 
(2001), education funding suffers in the presence of corruption. In this thesis, a comparison of 
the R 2 test between two OLS regressions was consistent with these authors. The dependent varia- 
ble is Ale, the HDL990 is the independent variable for the first equation and the Shadow Economy 
is added to the second equation. The R 2 of the augmented, second equation is higher, from 
40.62% to 48.44%. Schneider et al., (2010), Russell (2010), and others assert that unofficial eco- 
nomic activity, including those specific to the Shadow Economy, diverts income from the official 
GDP and toward unofficial economic activities (p. 5). The average Shadow Economy size for the 
thesis' sample set of countries is 30.32%. Last, variation in the AHDI, and AEEc, together ac- 
count for 54.04% of the variation in AIc Total . 

Education Inequality is especially limiting in developing nations as research shows the 
direct and positive relationship between good education and positive economic growth (La Porta 
& Shleifer, 2008). Beyond the education funding issue, education inequality affects nations 
through limiting access. Studies show diverging education between the wealthier and poorer 
while the governance system matures (TI, 2009b). 

Would a country be better off if education were a protected economic asset? Consider 
the long-term detrimental effects of inadequate education on economic development (Barro & 
Lee, 2001; Romer, 1986; Sen, 1984, 1997, 2004; Tanzi, 1998). One might compare the R 2 values 

131 



of the change in income per capita of two sets of transition economies that gained independence 
during the dissolution of the USSR. The Group A Countries adopted a democratic political sys- 
tem, embraced good governance and transparent government, and prioritized education in the 
spending on public goods. The Group B Countries did not. Hypothesis 1 is that the change in In- 
come Per Capita is higher in Group A. Hypothesis 2 is that duration of the Educational Kuznets 
Curve would be shortened, and hopefully, the divergence in education equality shorter. Measured 
by the rate of increase in economic growth between the two groups, the time period for economic 
recovery after the trough sparked by a regime or political shift (Rostow, 1991), Group A would 
outpace Group B. Additional work furthering the idea of the Educational Kuznets Curve is criti- 
cal. 

Future research that conjoins the officially and unofficially earned income would pro- 
mote better-informed policymaking at every level of government. Research that marries the 
income sources with planning in the policy budgeting process, policy analysis, stages of econom- 
ic growth, and economic cycle position would promote better-informed policy making, as well. 
Lastly, adding to the economic development planning process reminders that education's budget 
may be a target for corruption, and that education's budget, when protected, may produce increas- 
ing returns, is vital to informed, responsible policy. 



132 



APPENDIX A: COUNTRY BRIEFS 

Region 

Geographically, the sample set of countries lay in Central and Eastern Europe. The coun- 
tries chosen for this analysis where selected in part for their commonality, to minimize the scope 
of determining factors on governance. The countries occupy the former Eastern Bloc, the Balkan 
Peninsula, border the Adriatic Sea, or they border counties that do. The "Iron Curtain," figura- 
tively, is the veil "[fjrom Stettin in the Baltic to Trieste in the Adriatic" east of which reigned 
"Soviet influence" and an "increasing measure of control from Moscow" (Churchill, 1946). The 
effects of the iron curtain on European countries boarding or west of this "Soviet Sphere" (1946) 
may be different by physical magnitude or psychological impact, or, may be the same; the force 
field went up, and came down, on both sides of the veil. Likewise, construction, patrol, and dem- 
olition occurred on both sides of the Berlin Wall (Ofer, 1987). The sample set of countries, based 
on geography, are those most affected by the Soviet radiation outward from Moscow in concen- 
tric circles, and from its Satellite States. 

The sample countries are, and were, economically inter-dependent. Centuries of trade re- 
lationships and routes preceded these new alliances (Elisseeff, 1998), and the same or new routes 
opened after the dissolution of the former USSR (WTO, 2010f). (See Country Briefs). Similarly, 
migration routes and shifting empires facilitated the intermixing of nationalities, ethnicities, reli- 
gions, languages, customs, and disease (Diamond, 1997; Alesina et al., 2002). Thus, the sample 
set of countries share, in varying degrees, similar ethnolinguistic heritage. 
Socialist Countries in Central and Eastern Europe 

The goal in this section is to define Socialist as used in this thesis. According to Jonas 

Kornai (1993), literature and the media intertwine terms and confuse meanings for the political 

systems in Central and Eastern Europe. The term socialism "frequently used by politicians and 

by the press outside the socialist world is 'communist system' or simply 'communism'. . .[to re- 

133 



fer] to the political economy of communism" (p. 10). [He concludes,]. . .the choice of the term is 
a matter of semantics, as long as the meaning is clearly defined..." (p. 10). 

Socialist as used in this thesis refers to a sovereign nation's political economy, and is un- 
concerned with a traditional placement on a left-right or liberal-conservative spectrum. 
According to Kornai, a Socialist political economy centralizes the authority of planning the econ- 
omy and where the state also controls decision on "production and consumption, investment and 
saving,". . ."the distribution of income" and economic efficiency (1992, p. 4). For this thesis, 
Statism, and Fascism are both socialist, as are Socialism, Communism, Collectivism, Nazism, 
Marxism, Stalinism, and Leninism. 
Inclusion in the Sample Set of Countries 
Rule 1 : The country was or remains Socialist 

Sachs & Warner (1995) and Kornai (1993) among others deliberately include in data sets, 
countries which endured "at least several years" of Communist influence, and which that ruling 
party declared it was Socialist (Kornai, 1992, p. 4). For this thesis, it is paramount that the coun- 
tries endured at least several years of extraordinarily influence by the Soviet Empire, and has 
since endured the development effects of its demise, either positively or negatively. These effects 
may be cultural, shifts in population densities, related to trade relations before, during, or after the 
Cold War, shifts in economic strengths, political and governance systems, or a multitude of other 
factors either undiscovered or outside the scope of this thesis. 

Rule 2: Four or more years of Soviet influence, plus newly created, liberated, or re-gained sover- 
eignty, independence or the ability to trade, travel, and migrate 

Other scholars use different criteria to include or exclude in studies counties that occupy 
Central and Eastern Europe. Dividing the former USSR states into those that joined certain alli- 
ances such as the Commonwealth of Independent States (CIS), for example, or the later, the 
Collective Security Treaty Organization (CTSO), or the Eurasian Economic Community (EAEC), 

134 



is not workable over time, as each of these organizations has shifting membership. In addition, 
not one has always included all of the former USSR countries, and at no time has Hungary, Yu- 
goslavia, the Czech Republic, or Albania joined the organizations, according to the World Trade 
Organization (WTO, 201 Oh). Other methods to separate or group countries exist, for example, 
countries were dissected by factors such as ethnolinguistic homogeneity (Alesina et al., 2002), in- 
stability by number of coups (Barro, 1991), and religious affiliation (Barro & McCleary, 2003). 
Rule 3: Geographically related by inland border, trade route, or sea-trade route 

Geographically, the sample set of countries lay in Central and Eastern Europe. The coun- 
tries chosen for this analysis where selected in part for their commonality, to minimize the scope 
of determining factors on governance. The countries occupy the former Eastern Bloc or Soviet 
Sphere of Influence (Hirsch et al., 2002) or they border (significantly) counties that do. 

This thesis does not include China or other Asian countries as inclusion would require re- 
search and data for dummy variables on other geographic and cultural issues beyond the scope of 
this thesis. This country grouping is consistent with the Schneider et al. (2010). For the same 
reason, former or current communist countries that are geographically distant from the former 
USSR, such as North Korea are not included. 
Rule 4: Ethnolinguistically interrelated, Economically interdependent 

The Central and Eastern European countries are ethnolinguistically and economically 
linked, trade among and between these countries survived the Iron Curtain (Ofer, 1987), or was 
revived at its crumbling; each country was part of the Warsaw Pact, or of NATO, or the country 
was economically affected by the division between them. Two countries offer examples show 
extreme reasons for necessary inclusion to the sample set. 

The first example is Czechoslovakia in 1968. Unwilling to be part of the security alli- 
ance, it invaded by the Warsaw Pact members (save Romania), and inclusion in the Warsaw Pact 
was forced upon it. For several reasons including this, Czechoslovakia must be included in the 

135 



sample set (DOS, 2010b, p. Czechoslovakia). The second country is Italy, providing two exam- 
ples of ethnolinguistic fractionalization. Before and during WWII fifteen-hundred Italian men 
went to work for Volkswagen in Germany as expatriates, and were not allowed to return home. 
In fact, over seventy percent of the labor force of Germany consisted of foreigners, mostly from 
Poland and Italy, and Soviet prisoners of War (Burleigh, 1996, p. 43). Over Seventy -five hun- 
dred Italian Jews were victims of the Holocaust alongside the two million Soviet Jews and two 
million ethnic Poles (DOS, Russia, p. People). Prior to the Berlin wall closing migration, Ger- 
mans fled west. In the summer of 1991, during the "great East- West migration," over 35,000 
Albanians migrated to Italy to join family, gain employment, or seek asylum from the Albanian 
government (Bocker et al., 1998, p. 232). Millions of people migrated west after the fall of the 
Berlin Wall, creating or re-creating trading ties with the dawning of a new era in Central and 
Eastern Europe in the late 1980s and early 1990s (pp. 259 - 260). The economy of each county in 
the sample set was affected greatly by the dissolution of the USSR. The ethnic, cultural, reli- 
gious, or linguistic history differs by country, which effects economic growth (Alesina et al., 
2002). Russia's 139.4 million citizens descend from more than 100 ethnic groups (p. Russia, 
People), while over 92% of Hungarians claim Hungarian ethnicity (p. Hungary, people). Inclu- 
sion in the sample set required certain levels of Soviet influence, as well. Soviet influence, 
degree of heterogeneity, and economic interdependence provide a reasonable grouping to study 
(Alesina et al., 2002). 

Trade relations link these countries before and after the fall of the Berlin Wall. From a 
primarily closed economy in 1989, as of 2007, Russia is the thirteenth largest exporter and nine- 
teen largest importer of goods in the world, with trade relations between countries from both the 
NATO and Warsaw Pact trading alliances, and from both sides of the Iron Curtain. Netherlands 
buys 10.62% of Russian exports, Italy buys 6.46%, Germany 6.24%, China 5.69%, Turkey 4.3%, 
and Ukraine purchases 4.01% of the $303 billion in total exports. In 2008, Russia imported $191 

136 



billion in goods, Germany provided 14.39%, China 13.98%, Ukraine 5.48%, Italy 4.84%, and the 
US sent 4.46% of the total imports (CIA, 2009, p. Russia). 

The dissolution of the former USSR disrupted the economic equilibrium of Eastern Eu- 
rope. 

[There] has been the steep collapse of trade among the countries of the former 
Council for Mutual Economic Assistance (CMEA). In part, the collapse has re- 
sulted from a decline in Russian sales of oil and gas to Eastern Europe. In part, 
exports of military equipment to the region have declined. ... Overall, Russia's 
exports to the CMEA countries declined steeply, from an estimated $40. 1 billion 
in 1990 to $15.9 billion in 1991 (Lipton et al., 1992, p. 225). 

The breakup of the former USSR also brought on one of the most profound and far- 
reaching transformations of the twentieth century. The disintegration of the command structures 
in the old regimes triggered some of the most chaotic economic, political, and social changes in 
modern history Abed et al. (2002a). 



137 



Country Briefs 

The following Country Briefs present evidence and data relevant to this thesis on three 
groups of sovereign countries as of 2008. Group I consists of fifteen countries that occupy the 
geographic area of the former United Soviet Socialist Republics (USSR or SSR for an individual 
Republic) as of 1989. Group II consists of fifteen Eastern Bloc countries during and after World 
War II until 1998 (Beissinger, 2006). The term "Eastern Bloc" is "[t]he name applied to the 
former communist states of Eastern Europe, including Yugoslavia and Albania, as well as the 
countries of the Warsaw Pact" (Hirsch et al., 2002, p. 316). 

Group III is for data validation in this thesis. It consists of six countries extraordinarily 
influenced by the Soviet Empire, that maintain a communistic or socialistic authority, were occu- 
pied by or that operated as a Satellite State or Puppet State of the Soviet Empire. "A satellite 
state (sometimes referred to as a client state) is a political term that refers to a country that is for- 
mally independent, but under heavy political and economic influence or control by another 
country" (Hirsch et al., 2002, p. 316). Following is the rule of thumb for inclusion in Group III. 
The country: 1) is socialist, 2) was occupied by or allied with communist rulers through a satellite 
relationship, 3) is situated within Eastern Europe, Central Europe, or Central Asia and bordered 
the USSR, 4) shared strong ethnolinguistic, migration, and economic history (Elisseeff, 1998), 5) 
was not part of the USSR, and 6) maintains strong trade relationships with countries in Groups I 
and II (CIA, 2009). The countries in Group III are Austria, Finland, Greece, Turkey, Italy, and 
Cyprus. 

Unless otherwise noted, the six sources for corruption, governance, historical, and demo- 
graphic data and information follow. 1. The States Department of State (DOS) electronic public 
library, Countries and Regions: Background Notes by country (DOS, 2010c). 2. The Europa 
World Year Book (Europa) by country (Maher, 2008). 3. The 2006 United States Agency for In- 
ternational Development (USAID) Anti-Corruption Final Report (2006), the US Library of 

138 



Congress (2010), and (CIA2009a). Schneider et al. provide the Shadow Economy figures 
(Schneider et al., 2010a) unless noted otherwise. The Country Briefs assume 1990 for the pre-test 
year (HDI 1990 ), and 2007 for the post-test (HDI2007), unless otherwise noted. In each case, if 1990 
and 2008 data are not available, the data reported are the closest available to the test dates and are 
noted. Human development and economic statistics data originate from the Human Development 
Report (HDR) statistical database found in the 2010 report, or in prior year HDR reports as noted 
by the report year (2010). The 1993 HDR report provides HDT990, unless 1990 data are unavail- 
able, in which case this section cites the report where the data are available for the earliest 
possible year. However, this citation does not hold for the Human Development Index data. The 
HDI researchers made an adjustment in the methodology inversed the ranking order so that high 
rankings are equivalent to high human development levels after 1995, when the inverse was the 
standard in the initial years of the HDR. The Annex to the 2009 report, HDI Trends and Indica- 
tors (1980 - 2007), provide the HDI 19 9 (p. Annex). Table 1 (1993) reports the legacy Educational 
Attainment value, and Table H (2009) reports the HDI2007 value. Table GER reports the com- 
bined primary, secondary, and tertiary ratio, or the Gross Enrollment Ratio (HDR, 2009, p. Table 
GER) 

In the Country Briefs section, the GDP data are stated in terms of Purchasing Power Pari- 
ty (ppp), standardized to the United States dollar in 2000, as this is the methodology used by the 
HDR. The 2010 Human Development Report provides the demographic and economic data in its 
statistical database (HDR, 2010f). The United Nations Educational, Scientific, and Cultural Or- 
ganisation (UNESCO) provide the data on government expenditure on public education (2010). 
Pre-test Shadow Economy data are calculations for the average Shadow Economy size in the 
years 1990 - 1998, while the posttest Shadow Economy data are calculations for the average from 
1999 through 2008, as consistent with the method used in creating the Shadow Economy data set 
(Schneider etal., 2010a). 

139 



Group #1 - Countries of the former USSR 
The Baltics: Estonia, Latvia, Lithuania 

Duke Midaugas unified the Baltic tribes from 1236 to 1263 in current-day Lithuania. 
Grand Duke Gediminas ruled until 1341, and stretched Gediminas Dynasty from the Baltic to the 
Black Sea, spreading Christianity. By 1864 and until WWI, the Russian Empire had taken con- 
trol over the Baltics from Austria and Prussia. Lithuania and Latvia declared independence after 
WWI, and Estonia won independence in 1918 with the Peace Treaty of Tartu. German and Sovi- 
et forces occupied each during the interwar years. The German and Soviet non-Aggression Pact 
of 1939 brought the Baltics into the USSR. During the Cold War, the economies of the Baltic 
States were reorganized to benefit the Soviet needs into an urbanized industrial workforce build- 
ing military equipment. With the demise of the Soviet Empire, the Baltic States gained 
independence again in September 1991 (DOS, 2010, p. Balkans, History). 

Estonia 

Europa reports that Russian annexation of Estonia from Swedish rule in 1721 established 
Estonia's geographic boundaries. In 1944, Soviet troops began the Estonian 'Sovietization'." 
"By the end of 1949, most Estonian farmers had been forced to join collective farms. Investment 
concentrated on electricity generation and the chemicals sector expanded heavy industry. Struc- 
tural change in the economy was accompanied by increased political repression. . ." (Maher, 2008, 
p. 1585). "Even before it regained independence. . ., Estonia had begun a transition to a market 
economy. However, despite Estonia's relative prosperity during the Soviet period, the collapse of 
the USSR and its internal economic system resulted in serious economic difficulties. The annual 
rate of inflation reached 1,076% in 1992" (p. 1586). August 20, 1990, marks the re-independence 
of the Republic of Estonia as a democratic state (CIA, p. Estonia). 

In 1990, Estonia's population was 1.57 million, 96.0% of those ages 15 and above were 
literate. The combined gross enrollment was 81.5%. Of government expenditures, 25.5% were 

140 



dedicated to public education. The education index was 2.66. The Life Expectancy index was 
.740, life expectancy at birth was 69.4 years. The GDP was $5.99 billion, $3,822 per capita, for a 
GDP index value of .781. The GDP per capita hit its low in 1993, registering $2,744, rebounding 
by 2001 to exceed levels prior to the USSR breakup. The average annual growth GDP per capita 
rate from 1989 to 2008 was 8.9%. The HDI in 1990 was .817, and the Shadow Economy equaled 
34.3% of the total GDP. 

In 2008, the population of Estonia decreased to 1.34 million. On average, 99.8% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
91.2%. The education index was .964; 14.78% of Estonia's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .799 with expectancy of 72.9 years. GDP In- 
dex was .887, at $9.53 billion and $7,1 14 per capita. The HDI increased to .883 or 40th in the 
world. Estonia's estimated underground economy from 1999 to 2008 averaged 40.3%, which 
translates to $3.8 billion in 2008, bringing the gross GDP to $13.8 billion and GDP per capita, 
$9,980 per person. 

Latvia 

According to DOS, Latvia did not enjoy a time of territorial sovereignty prior to Novem- 
ber 18, 1918, when the Latvian People's Council declared its independence, which was lost again 
to the USSR until August 21, 1991 (p. Latvia, History). During the Cold War, Latvia maintained 
some economic viability as a trade route to the north via the Baltic Sea, and capitalized on its in- 
digenous resources of timber and agriculture products. Latvia embraced free market reforms and 
transparency since reestablishing its independence (p. Economy). Latvia lost a third of its popu- 
lation to the Holocaust (p. History). 

In 1990, Latvia's population was 2.67 million, 96.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 73.7%. Of government expenditures, 16.79% were 
dedicated to public education. The education index was 2.66. The Life Expectancy index was 

141 



.734 and life expectancy at birth was 69. 1 years. Latvia's GDP was $10.41 billion or $3,901 per 
capita, for a GDP index value of .771. The HDI in 1990 was .803. The HDI in 1990 was .817, 
and the Shadow Economy equaled 25.7% of the total GDP. The average annual growth of the 
GDP per capita from 1989 to 2008 was 6.7%. The GDP per capita hit its low in 1993, registering 
$2,271, and rebounded by 2004 to exceed levels prior to the USSR breakup. 

In 2008, the population of Latvia increased to 2.27 million. On average, 99.8% of those 
ages 15 and above were literate in the years from 1999-2008, with combined gross enrollment of 
90.2%. The education index was .961; 22.66% of Latvia's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .788 with expectancy of 72.3 years. GDP In- 
dex was .851, at $13.67 billion and $6,036 per capita. The HDI increased to .866 or 48th in the 
world. Latvia's estimated underground economy from 1999 to 2008 averaged 41.7%, which 
translates to $5.7 billion in 2008, bringing the gross GDP to $19.38 billion and income per capita, 
$8,553 per person. 

Lithuania 

According to DOS, Lithuania regained independence on February 4, 1991 from the 
USSR. The Lithuanians posted the greatest relative population loss to the Holocaust. As a Re- 
public, Lithuania's economic position was manufacturing and trade for the Soviet Union. After 
the USSR demise, the inefficient infrastructure could not compete with world manufacturing, and 
Lithuania relied on the former USSR for 90% of its exports. By 1997, only 47% of the exports 
shipped to the Soviet States, and the market reforms toward private enterprise had begun to work. 
Lithuania has an ice-free seaport, which ferries goods and traffic to Swedish, Danish, and German 
ports. As of 2008, Lithuania has strong growth in the technology and service sectors along with a 
record of democratic voting (p. Lithuania, History). 

In 1990, Lithuania's population was 3.7 million, 96.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 74.6%. Of government expenditures, 21.8% were 

142 



dedicated to public education. The education index was 2.66. The Life Expectancy index was 
.763 and life expectancy at birth was 70.8 years. Lithuania's GDP was $15,866 billion or $4,041 
per capita, for a GDP index value of .816. The HDI in 1990 was .828, and the Shadow Economy 
equaled 26% of the total GDP. The GDP per capita hit its low in 1993, registering $2,435, and 
rebounded by 2003 to exceed levels prior to the USSR breakup. The average annual growth rate 
in GDP per capita from 1989 to 2008 was 4.3%. 

In 2008, the population of Lithuania decreased to 2.27 million. On average, 99.7% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 92.3%. The education index was .968; 14.58% of Lithuania's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .780 with expectancy of 71.8 
years. GDP Index was .863, at $20.25 billion and $5,154 per capita in US 2000 constant dollars. 
The HDI increased to .870 or 46th in the world. Lithuania's estimated underground economy 
from 1999 to 2008 averaged 31.9%, which translates to $6.48 billion in 2008, bringing the gross 
GDP to $26.72 billion and income per capita, $6,798 per person. 
Central Asia: Kazakhstan and Turkestan - Tajikistan, Turkmenistan, Kyrgyzstan, and Uzbekistan 

The vast area of Turkestan covers the geographic boundaries of Uzbekistan, Turkmeni- 
stan, Tajikistan, and Kyrgyzstan, Afghanistan, and Mongolia (Humboldt, 1843). Characterizing 
the region are nomadic peoples and the Silk Road overland travel route connecting Asia with Eu- 
rope. The region fell under the rule of Genghis Khan by 1227 and remained occupied by various 
Mongolian rulers until the Russian Empire took control of all but Mongolia (Elisseeff, 1998). In 
1727, Russia and Manchu China concluded the Treaty of Khaiaka detailing the border between 
China and Mongolia that exits in large part today (Dos, 2010, p. Mongolia). Mongolia and Ka- 
zakhstan create over 10,000 kilometers of Russia's southern border. All of Turkestan was under 
communism after WWI. Mongolia's model for its communist government was the Soviet model. 
From 1920 until the 1980s, Mongolia aligned itself with the Soviet Union for continued military 

143 



assistance against China (p. Mongolia). Since its 1990 independence, Mongolia has shifted to- 
ward a market economy, electing non-communist leaders starting in the 1993 elections, and has 
increased foreign relations with China, the US, and other industrialized nations (p. Mongolia). 
The Shadow Economy of the Kazakhstan and Turkestan regions grew from 25.88% of the total 
GDP in 1990 to 40.57% in 2008 (Schneider, 2010, Tables 1-3). 

Tajikistan 

Tajikistan's geographic boundaries date back to the Samanid Empire (A.D. 875 - 999); 
however, the Mongol and then Russian Empires honored no boundaries. In 1920, the area came 
under Soviet rule as part of Uzbekistan. Tajikistan gained autonomy as a Soviet Socialist Repub- 
lic in 1929. From 1992 through 1997, Tajikistan suffered ongoing civil war, decimating its 
economic infrastructure, and remains the poorest country of the former Soviet Union. By 2008, 
the political situation suffered from a lack of transparency, and flawed elections. "Government 
interference in the economy and massive corruption stifle economic growth and private invest- 
ment" in Tajikistan (p. Economy). 

In 1990, Tajikistan's population was 5.3 million, 93.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 77.5%. Of government expenditures, 29.3% were 
dedicated to public education. The education index was 2.25. The Life Expectancy index was 
.632 and life expectancy at birth was 62.9 years. Tajikistan's GDP was $2,259 billion or $426 
per capita, for a GDP index value of .581. The HDI in 1990 was .636, and the Shadow Economy 
figure was 25.88% of the total GDP. The GDP per capita hit its low in 1996, registering $122, 
where it remained through 1997. Tajikistan is one of five countries in the sample set with a GDP 
per capita that, as of the data gathered for 2008, has yet to rebound to levels seen prior to the 
USSR breakup. Moldova, Kyrgyzstan, the Ukraine, and Serbia are the other countries. The aver- 
age annual growth rate in GDP per capita from 1989 to 2008 was -2.8%. 



144 



In 2008, the population of Tajikistan increased 6.84 million. On average, 99.6% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
70.9%. The education index was .896; 17.1% of Tajikistan's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .691 with expectancy of 66.4 years. GDP In- 
dex was .474, decreased 26% to $1.67 billion and $245 per capita. The HDI increased to .688 or 
127th in the world. Tajikistan's estimated underground economy from 1999 to 2008 averaged 
44.3%, which translates to $742 million in 2008, bringing the gross GDP to $2.42 billion and in- 
come per capita, a meager $353 per person, the lowest income per person in the sample set. 

Turkmenistan 

According to DOS, Turkmenistan was the home of the "powerful Turks of the Seljuk 
Empire" from the middle of the 1 1 th century until it broke down in the late 12 th century. The 
"Turkmen lost their independence when Genghis Khan took control of the eastern Caspian Sea 
region on his march west" (p. History). Intertribal wars and rule by various empires over the next 
seven centuries depleted the strength of the Turkmen. As the Russian Empire's strength waned, 
the Soviet Empire overtook Turkmenistan by 1924 (p. History). Although its economy has prom- 
ise due to vast natural gas reserves, it is corrupt, and deeply connected to centralized planning. 
Researchers are unable to quantify the degree of corruption in Turkmenistan. The Red Cross and 
international organizations maintain a presence in Turkmenistan to thwart ongoing human rights 
and political violations (p. Political Conditions). 

In 1990, Turkmenistan's population was 3.67 million, 93.0% of those ages 15 and above 
were literate, with no gross enrollment data available. Of government expenditures, 24.5% were 
dedicated to public education. The Education Index was 2.25. Life Expectancy index was .629; 
life expectancy at birth was 62.8 years. Turkmenistan's GDP was $3.82 billion or $1,042 per 
capita, with no GDP index value available. The 1990 HDI for Turkmenistan is .73. The GDP per 
capita hit its low in 1997, registering $455, and rebounded by 2004 to exceed levels prior to the 

145 



USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 
2.65%. The HDI in 1990 was .817, and the Shadow Economy equaled 24 % of the total GDP. 

In 2008, the population of Turkmenistan increased to 5.4 million. On average, 99.5% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 73.9%. The education index was .906; Turkmenistan's 2000-2008 government 
expenditure on education averaged 24.5% of total expenditures. The Life Expectancy Index was 
.661 with expectancy of 64.6 years. GDP Index was .651. GDP rose to 8.64 billion and $1,714 
per capita. The HDI was .739 or 109th in the world. Turkmenistan's underground economy es- 
timation was 36%, and the gross GDP estimation was $1 1.76 billion in US constant year 2000 
dollars, which translates to a gross figure of $2,331 Total per capita income. 

Uzbekistan 

Uzbekistan's geographic boundaries were created by the Soviets in 1924 from the territo- 
ries of three "leading cities along the Silk Road, Bukhara, Khiva, and Samarkand" (DOS, p. 
People). This territory is 90% Sunni Muslim and about 80% Uzbek. The economy of Uzbekistan 
relies on natural resources, cotton for exports, and manufacturing for the Russian market. The 
2007 GDP/c is 25% less than 1990 figures. The economic depression is a result of tight govern- 
mental control over industry and cronyism (p. Economy). "The constitution of Uzbekistan 
provides for separation of powers, freedom of speech, and representative government. In reality, 
the executive holds almost all the power. . . [and] selects and replaces provincial governors (p. 
Political Conditions). "None of . . . [the 1991 - 2008] elections or referenda were deemed free or 
fair" elections by the Organisation for Security and Co-operation in Europe (OSCE) (p. Political 
Conditions). 

In 1990, Uzbekistan's population was 20.51 million, 93.0% of those ages 15 and above 
were literate, with a combined gross enrollment of 75.6%. Of government expenditures, 22.84% 
were dedicated to public education. The education index was 2.25. The Life Expectancy index 

146 



was .697 and life expectancy at birth was 66.8 years. Uzbekistan's GDP was $14.04 billion or 
$685 per capita, for a GDP index value of .5 10. The HDI in 1990 was .687. GDP per capita hit 
its low in 1996, registering $499, and rebounded in 2006 to exceed levels prior to the USSR 
breakup, its average annual growth rate from was 1.08%. The Shadow Economy equaled 22.1 % 
of the total GDP or $3.1 billion in US equivalent dollars in 2000. 

In 2008, the population of Uzbekistan increased to 27.31 million. On average, 96.9% of 
those ages 15 and above were literate in the years from 1999-2008, with combined gross enroll- 
ment of 72.7%. The education index was .888; Uzbekistan's 2000-2008 government expenditure 
on education was 22.84% of total government expenditures. The Life Expectancy Index was .711 
with expectancy of 67.6 years. GDP Index was .532, the GDP increased to $22.93 billion; how- 
ever, and the GDP per person to $840. The HDI increased to .710 or 1 19th in the world. 
Uzbekistan's estimated underground economy from 1999 to 2008 averaged 37.93%, which trans- 
lates to $8.69 billion in 2008, bringing the gross GDP to $37.93 billion and income per capita, 
$1,158 per person. 

Kazakhstan 

According to DOS, Kazakhstan provided the coal for the USSR, which moved its indus- 
trial sectors closer to Kazakhstan for efficiency. This changed the ethnic makeup of the country 
when the Kazakhstan Kazakh people (renamed Kyrgyz by the Soviets so they would have the 
same name as the Kyrgyz people in Kyrgyzstan) became a minority ethnic group to Russians who 
were displaced to work and oversee coal production. This is the only former Soviet state where 
the indigenous population became the minority (p. Economy). It is the largest land-locked coun- 
try in the world, sharing with Russia its northern border, over 6,800 kilometers. As of 2005, the 
elections did not meet the OSCE standards. The economy is growing and healthy, with energy its 
leading sector (p. Economy). 



147 



In 1990, Kazakhstan's population was 16.35 million, 93.0 % of those ages 15 and above 
were literate, with a combined gross enrollment of 80.0%. Of government expenditures, 18.88% 
were dedicated to public education. The education index was 2.25. The Life Expectancy index 
was .696 and life expectancy at birth was 66.7 years. Kazakhstan's GDP was $26.34 billion or 
$1,612 per capita, for a GDP index value of .721. The HDI in 1990 was .778. 

In 2008, the population of Kazakhstan increased to 37.3 million. On average, 99.6% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 91.4%. The education index was .965; 13.26% of Kazakhstan's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .667 with expectancy of 64.9 
years. GDP Index was .782, increased an average of 2% to $37.3 billion and $2,380 per capita. 
The HDI increased to .804 or 82th in the world. Kazakhstan's estimated underground economy 
from 1999 to 2008 averaged 45.3%, which translates to $16.8 billion in 2008, bringing the gross 
GDP to $54.2 billion and income per capita, $3,458 per person. 

Kyrgyzstan 

Kyrgyzstan's geographic boundaries created by the Soviets in 1926 are roughly the bor- 
ders of the territories inhabited by the Kyrgyz people as of the 16 th century (DOS, 2010, p. 
History). In 1989, Kyrgyzstan citizens voted to remain faithful to the '"renewed federation'" (p. 
History) of the USSR. In 1990, 89% Kyrgyzstan's exports of went to the USSR, mostly agricul- 
ture and coal (p. Economy). Corruption has plagued politics in Kyrgyzstan, with the 2005 
election improved but not acceptable by election commission of the OSCE community (p. Gov- 
ernment). 

In 1990, Kyrgyzstan's population was 4.42 million, 93.0 % of those ages 15 and above 
were literate, with a combined gross enrollment of 77.5%. Of government expenditures, 23.1% 
were dedicated to public education. The education index was 2.25. The Life Expectancy index 



148 



was .688 and life expectancy at birth was 66.3 years. Kyrgyzstan's GDP was $2.05 billion or 
$465 per capita, for a GDP index value of .547. The HDI in 1990 was .687. 

In 2008, the population of Kyrgyzstan increased to 5.28 million. On average, 99.3% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 77.3%. The education index was .918; 18.25% of Kyrgyzstan's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .710 with expectancy of 67.6 
years. GDP Index was .500, decreased an average of 1.07% per year to $2.0 billion and $379 per 
capita. The HDI increased to .710 or 120th in the world. The estimated underground economy 
from 1999 to 2008 averaged 42.0%, which translates to $804 million in 2008, bringing the gross 
GDP to $2.4 billion and income per capita, $538 per person. 
Eastern Europe: Belarus, Moldova, Poland, Romania, Russian Federation, and Ukraine 

Belarus 

Belarus 's geographic boundaries today were approximately those created in 1939 with 

land seized during the Soviet invasion of Poland unified with the Belorussian SSR (Maher, 2004, 

p. 713). Belarus was "one of the most prosperous in the USSR, with a wider variety of consumer 

goods available than other republics (p. 713). Belarus was an original member of the USSR in 

1922. The following is from DOS (2010c, p. Belerus). 

Occupied by the Russian empire from the end of the 18th century until 1918, 
Belarus declared its short-lived National Republic on March 25, 1918, only to be 
forcibly absorbed by the Bolsheviks into what became the Soviet Union. ... It 
declared its sovereignty on July 27, 1990, and independence from the Soviet Un- 
ion on August 25, 1991, and independence from the Soviet Union on August 25, 
1991. 

By 1995, Belarus' s economy was 61% off 1990 levels, and had recovered by 2000. Bela- 
rus 's economic downturn was due in part to the lack of an independent economic infrastructure 
and trade lost to neighboring countries with dissolution of the Soviet economic system, and was 
minimized in part by private market forces as operating successfully as black markets to the 
communist economy (Maher, 2004). By 2008, elections failed to meet OSCE standards follow- 

149 



ing two years of protests for increased energy security and pressure on the government to "meet 
12 terms that had been stipulated by the European Commission in November 2006 as being con- 
ditional to Belarus's access to greater aid and trade co-operation within the European 
Neighborhood Policy" (DOS, 2010c, p. Political Conditions). 

In 1990, Belarus's population was 10.19 million, 95.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 80.2%. Government expenditures on education 
were 17.1% of total government expenditures. The education index was 2.47. The Life Expec- 
tancy index was .760 and life expectancy at birth was 70.6 years. Belarus's GDP was $14.36 
billion, $1,410 per capita, for a GDP index value of .705. The HDI in 1990 was .795. GDP per 
capita hit its low in 1995, registering $920, and rebounded in 2002 to exceed levels prior to the 
USSR breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was 
3.09%. The Shadow Economy equaled 35.6% of the total GDP or $5.1 billion in US equivalent 
dollars in 2000. 

In 2008, the population of Belarus decreased to 9.68 million. On average, 99.7% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
90.4. The education index was .961; 1 1.263% of Belarus's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .733 with expectancy of 69.0 years. GDP In- 
dex was .782, increased to 24.34 billion and $2,515 per capita. The HDI increased to .828 or 68th 
in the world. Belarus's estimated underground economy from 1999 to 2008 averaged 49.8%, 
which translates to $12.12 billion in 2007, bringing the gross GDP to $36.47 billion and income 
per capita, $3,767 per person. 

Moldova 

According to DOS, Moldova's geographic boundaries were created when it gained its in- 
dependence in 1991. The boundaries are consistent with those formed in 1940 by the USSR and 
roughly those of Bessarabia since the 13 th century Moldova was formerly part of the Mongol and 

150 



Ottoman Empires, offering a southern overland passage for the Silk Road (p. History). The GDP 
in 2007 was 56% of its 1990 height. Moldova is one of the poorest countries in Europe, having 
lost much of its market for its main export to the USSR, wine. Corruption, state-sponsored me- 
dia, and ineffective law enforcement plague Moldova, and the elections through 2008 were 
unacceptable to the Operation for Security and Co-Operation in Europe (OSCE) (p. Government). 

In 1990, Moldova's population was 4.36 million, 95.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 69.9%. Government expenditures on education 
were 22.9% of total government expenditures. The education index was 2.38. The Life Expec- 
tancy index was .709 and life expectancy at birth was 67.6 years. Moldova's GDP was $3.61 
billion or $980 per capita, for a GDP index value of .619. The HDI in 1990 was .735. GDP per 
capita hit its low in 1999, registering $346, and has yet to rebound to exceed levels prior to the 
USSR breakup. 

In 2008, the population of Moldova decreased to 3.63 million. On average, 99.2% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 71.6%. The education index was .899; 20.2% of the 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .722 with expectancy of 68.3 years. GDP In- 
dex was .541 ; its income per capita fell to $2. 1 1 billion and $591 per capita, one of the worst 
performing economies coming out of the former USSR. The HDI decreased to .720 or 1 17th in 
the world. The estimated underground economy from 1999 to 2008 averaged 45.8%, which 
translates to $967 million in 2008, bringing the gross GDP to $3.08 billion and income per capita, 
$862 per person, an average annual growth of 2.6%. 

Poland 

Poland was reconstructed in 1918, during the Treaty of Versailles, to roughly the bounda- 
ries of the laid out by King Mieszko I in 966. Poland united with Lithuania and together 
occupied the geography of today's Baltic region, Ukraine, Belarus, and parts of Russia until 1795. 

151 



Poland was partitioned between Austria, Prussia, and Russia from 1795 to 1918. From 1918 to 
1939, Poland was independent, but was split between Germany and USSR in 1939, as a result of 
the Molotov-Ribbentrop Pact, the non-aggression pact between the Soviet Union and Germany 
following WWII. From 1945 to 1989, Poland was part of the Soviet Empire. In 2007, 98% of 
Poland's population ethnically Polish (DOS, 2010, p. History). Poland's economy is healthy, di- 
verse, and growing, and as of 1996, its election processes pass the OCSE standards (p. Economy). 

In 1990, Poland's population was 38.12 million, 96% of those ages 15 and above were 
literate, with a combined gross enrollment of 75.5%. Government expenditures on education 
were 12.2% of total government expenditures. The education index was 2.57. The Life Expec- 
tancy index was .769 and life expectancy at birth was 71.1 years. The GDP was $118 billion or 
$3,097 per capita, for a GDP index value of .738. The HDI in 1990 was .806. The GDP per capi- 
ta low was in 1992, at $2,936, and rebounded in 1994 to exceed levels prior to the USSR breakup. 

In 2008, the population of Poland increased slightly to 38.2 million. The loss of roughly 
two million ethnic Poles to the holocaust partly offset population. On average, 99.3% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
87.7. The education index was .952; 12.12% of Poland's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .842 with expectancy of 75.5 years. GDP In- 
dex was .847; the GDP was $237 million and $6,228. The HDI increased to .880 or 41st in the 
world. Poland's estimated underground economy from 1999 to 2008 averaged 28%, which trans- 
lates to $66 billion in 2008, bringing the gross GDP to $303 billion and income per capita, $7,972 
per person, an average annual increase of 3.7%. 

Romania 

According to DOS, the Treaty of Berlin created Romania's geographic boundaries in 
1881. Today, 89% of its population are ethnic Romanians and affiliate themselves with the Ro- 
manian Orthodox church (p. People). "Romania's 1991 constitution proclaims Romania a 

152 



democracy and market economy," and claims as some of its values, human dignity, civil rights 
and freedoms, and justice (p. Government). 

In 1990, Romania's population was 23.21 million, 95.0% of those ages 15 and above 
were literate, with a combined gross enrollment of 66.4%. Government expenditures on educa- 
tion were 13.6% of total government expenditures. The education index was 2.47. The Life 
Expectancy index was .740 and life expectancy at birth was 69.4years. Romania's GDP was 
$43.98 billion or $1,896 per capita, for a GDP index value of .752. The HDI in 1990 was .786. 
GDP per capita hit its low in 1992, registering $1,553, and rebounded in 2004 to exceed levels 
prior to the USSR breakup. 

In 2008, the population of Romania decreased to 21.51 million. On average, 97.6% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 79.2. The education index was .915; 1 1.55% of Romania's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .792 with expectancy of 72.5 
years. GDP Index was .804, growing to $61.19 billion and $2,845 per capita, an annual average 
increase of 2.1%. The HDI increased to .837 or 63rd in the world. Romania's estimated under- 
ground economy from 1999 to 2008 averaged 36.3%, which translates to $22.2 billion in 2008, 
bringing the gross GDP to $83.41 billion and income per capita, $3,877 per person. 

Russian Federation 

According to the DOS, as of 2007, Russia's 139.4 million citizens descend from more 
than 100 ethnic groups speaking six languages and many dialects (p. People). Russia continues to 
reform its government into a modern system with "a president who wields considerable executive 
power. . .no vice president, and the legislative branch is far weaker than the executive" (p. Gov- 
ernment). From 1994 through 2008, Russian forces fought two wars and many skirmishes with 
the Chechens, for the territory of Chechnya, which threatened to recede along with the rest of the 
Caucasian states (p. Russian Federation). This geographic area was of interest to the new Russian 

153 



Federation for its vast oil reserves. Chechnya, having not been a separate state of the USSR, did 
not qualify for separate nation status under the new Soviet constitution (p. Russian Federation). 
The market reforms toward a freer market system in the wake of the demise of the USSR have 
not taken hold. Tight control over industry, corruption, and high tariffs helped cause several 
years of hyperinflation. Recent increases in oil revenues after a tax collection overhaul have so- 
lidified the economy on surer ground (p. Economy). 

In 1990, Russia's population was 148.29 million, 94% of those ages 15 and above were 
literate, with a combined gross enrollment of 83.7%. The education index was 2.61. The Life 
Expectancy index was .714 and life expectancy at birth was 67.9 years. Russia's GDP was 
$385.9 billion or $2,602 per capita, for a GDP index value of .817. The HDI in 1990 was .821, 
and the Shadow Economy equaled 27.8% of the total GDP. The GDP per capita hit its low in 
1998, registering $1,51 1, and rebounded by 2007 to exceed levels prior to the USSR breakup. 
The average annual growth rate in GDP per capita from 1990 to 2008 was .083%. 

In 2008, the population of Russia decreased to 141.95 million. On average, 99.5% of 
those ages 15 and above were literate from 1999-2007, with combined gross enrollment of 81.9. 
The Education Index was .933; 1 1.6% of Russia's 2000-2008 government expenditure was on 
education. The Life Expectancy Index was .686 with expectancy of 66.2 years. GDP Index was 
.833, with GDP at $432 billion and $2,602 per person. The HDI decreased to .817 or 71st in the 
world. Russia's estimated underground economy from 1999-2008 averaged 48.6%, translating to 
$209 billion in 2008, bringing the gross GDP to $641 billion and income per capita, $4,523 per 
person. 

Ukraine 

Ukraine's geographic boundaries created by the Treaty of Pereiaslav in 1654 are now the 
same, after the 1939 reunification of Galicia-Volhynia, and the 1954 of transfer of Crimea from 
the USSR back to Ukraine. It was one of the founding republics of the Soviet Union in 1922. 

154 



From 1961 through 1989, Ukraine produced about 25% of the total agricultural output of the 
USSR, and lead Europe in technology research, and industrial and steel manufacturing for the 
arms, mining and transportation industries. The population is over 75% ethnic Ukrainian. Many 
of the Soviet officials came from the Ukraine, including Leonid Brezhnev, leader of the Soviet 
party from 1964 - 1982, and the ruling clans remain powerful (USAID, 2006; CIA, 2009a). The 
Chernobyl Nuclear Power Plant explosion in 1986 marked the start of a deep economic trough. 
Ukraine's Act of Independence, on August 24, 1991 by a then newly elected parliament, marked 
the start of Ukraine as a democratic state. By 1999, Ukraine's economy shrunk to about 40% of 
its 1990 levels, due in part to the economic infrastructure and trade lost to neighboring countries 
and the dissolution of the Soviet economic system, and due in part to corruption's unwillingness 
to embrace a transparent, market -based system. By 2008, the political process had begun to stabi- 
lize, after public scrutiny for election fraud and corruption leading up to the 2004 elections 
(CIA,2009a). 

In 1990, Ukraine's population was 51.89 million, 93.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 77.9%. Government expenditures on education 
were 24.35% of total government expenditures. The education index was 2.30. The Life Expec- 
tancy index was .745 and life expectancy at birth was 69.7 years. Ukraine's GDP was $71.95 
billion or $1,387 per capita, for a GDP index value of .742. The HDI in 1990 was .754, and the 
Shadow Economy equaled 29.4 % of the total GDP. The GDP per capita hit its low in 1998, 
$590, and has not yet rebounded to its levels prior to the USSR breakup. The average annual 
growth rate in GDP per capita from 1990 to 2008 was -.095%. 

In 2008, the population of Ukraine decreased to 46.26 million. On average, 99.7% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 90. The education index was .960; 53.9% of Ukraine's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .720 with expectancy of 68.2 

155 



years. GDP Index was .707; the GDP was $53.46 billion and $1,156 per capita. The HDI in- 
creased to .796 or 85th in the world. Ukraine's estimated underground economy from 1999 to 
2008 averaged 53.9%, which translates to $28.8 billion in 2008, bringing the gross GDP to $82 
billion and income per capita, $1,794 per person. 
Transcaucasia: Armenia, Azerbaijan, and Georgia 

Armenia 

According to DOS, the Republic of Armenia's geographic boundaries today are those 
created in 1828 by the Russian Empire. Armenia signed its original Declaration of Independence 
from the Ottoman Turks on May 28, 1918. In 1920, the Soviet Red Army declared Armenia a 
Soviet Republic. Armenia reclaimed her sovereignty from the USSR on August 23, 1990. Indus- 
try accounted for seventy percent of Armenia's Soviet-run economy, as the second most densely 
populated region provided the needed labor, leaving relatively few acres available for agricultural 
production; only twenty percent of 1989 GDP was due to agricultural production. By 1993, Ar- 
menia's economy was 40% off 1989 levels, due in part to the trade lost to neighboring countries 
with dissolution of the Soviet economic system, the lack of a self-sufficiency due to reliance on 
imports for food and exports for GDP, and, being land-locked, reliance on relationships with 
neighboring countries for trade and travel. Azerbaijani and Turkish forces blocked rail traffic in 
1992, nearly bringing its economy to a standstill. The economy still struggles with corruption's 
unwillingness to embrace a transparent, market-based system (p. Economy). Through 2008, the 
political process remained unstable after public scrutiny for election fraud and corruption leading 
up to the early 2008 elections (p. Armenia). 

In 1990, Armenia's population was 3.54 million, 93.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 74.1%. Government expenditures on education 
were 20.50% of total government expenditures. The education index was 2.25. The Life Expec- 
tancy index was .715 and life expectancy at birth was 67.9 years. Armenia's GDP was $2.8 

156 



billion or $709 per capita, for a GDP index value of .574. The HDI in 1990 was .731, and the 
Shadow Economy equaled 40.3% of the total GDP. The GDP per capita hit its low in 1992, reg- 
istering $392, and rebounded by 2002 to exceed levels prior to the USSR breakup. The average 
annual growth rate in GDP per capita from 1990 to 2008 was 3.2%. 

By 2008, the population of Armenia decreased by 440,000 to 3.08 million. On average, 
99.5% of those ages 15 and above were literate in the years from 1999-2007, with combined 
gross enrollment of 74.6. The education index was .909; 13.2% of Armenia's 2000-2008 gov- 
ernment expenditure was on education. The Life Expectancy Index was .810 with expectancy of 
73.6 years. GDP Index was .675, increased to $4.67 billion and $1,299 per capita. The HDI in- 
creased to .798 or 84th in the world. Armenia's estimated underground economy from 1999 to 
2008 averaged 48.7%, which translates to $2.27 billion in 2008, bringing the gross GDP to $7.82 
billion and income per capita, $2,487 per person. 

Azerbaijan 

According to documents at the DOS, the Russian Empire created Azerbaijan Democratic 
Republic's geographic boundaries in 1828. Like Armenia, Azerbaijan signed its original Declara- 
tion of Independence from the Ottoman Turks on May 28, 1918; in 1920, the Soviet Red Army 
declared Azerbaijan a Soviet Republic. Azerbaijan reclaimed her sovereignty from the USSR on 
August 30, 1990. Unlike many former Soviet states, Azerbaijan is economically viable, pos- 
sessing two important advantages: vast oil reserves, and a coastline on the Caspian Sea. 
Azerbaijan's economy was in turmoil through the 1990s due in part to the lack of an independent 
economic infrastructure and trade lost to neighboring countries following the dissolution of the 
Soviet economic system. As a Soviet Republic, it provided mostly agricultural products to the 
USSR to the detriment strengthening its industrial sector. Heavy industries are state-owned and 
planned by Azerbaijan's central government. The transition into an oil exporting country exacer- 
bated the turmoil and hyperinflation accompanied the rapid expansion into the oil industry. By 

157 



2008, the elections largely conformed to the Organization for Security and Cooperation in Europe 
(OSCE) standards (p. Azerbaijan). 

In 1990, Azerbaijan's population was 7.16 million, 93% of those ages 15 and above were 
literate, with a combined gross enrollment of 72.1%. Government expenditures on education 
were 26.6% of total government expenditures. The education index was 2.25. The Life Expec- 
tancy index was .677 and life expectancy at birth was 65.6 years. Azerbaijan's GDP was $3.21 
billion or $978 per capita, for a GDP index value of .654. The HDI in 1990 was .755, and the 
Shadow Economy equaled 36.3% of the total GDP. The GDP per capita hit its low in 1995, reg- 
istering $488, and rebounded by 2006 to exceed levels prior to the USSR breakup. The average 
annual growth rate in GDP per capita from 1990 to 2008 was 2.8%. 

By 2008, the population of Azerbaijan grew to 8.68 million. On average, 99.5% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
66.2. The education index was .881; 18.52% of Azerbaijan's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .751 with expectancy of 70 years. GDP Index 
was .728, GDP increased to $5.73 billion and $1,825 per person. The HDI increased to .787 or 
86th in the world. Azerbaijan's estimated underground economy from 1999 to 2008 averaged 
63.3%, which translates to $1 1.7 billion in 2008, bringing the gross GDP to $30.21 billion and in- 
come per capita, $3,840 per person. 

Georgia 

Georgia's geographic boundaries were created "on May 26, 1918, in the wake of the Rus- 
sian Revolution" (DOS, 2010, p. History). After 2,200 years of occupation and shifting empires, 
the population remains cohesive with over 82% of its population being ethnic South Caucasians 
and Georgian Orthodox (p. History). From 1961 through 1989, Georgia was one of the most 
prosperous states of the USSR. "Political turmoil following Georgia's independence had a cata- 
strophic effect of the country's economy. The cumulative decline in real GDP is estimated to 

158 



have been more than 70% between 1990 and 1994" (p. Economy). Georgia exported 100% of 
some fruits and vegetables to the USSR until 1990. Turkey is now its biggest trading partner. 
Georgia has the highest rate of Shadow Economy in the world, at 68%, with a government 
"marked by rampant cronyism, corruption, and mismanagement" (p. Economy). New reforms 
and a flat tax structure increased tax collection from 17.8% in 2004 to 22.2% in 2008. 

In 1990, Georgia's population was 5.46 million, 93% of those ages 15 and above were 
literate, with a combined gross enrollment of 78.3%. Government expenditures on education 
were 6.9% of total government expenditures. The education index was 2.25. The Life Expectan- 
cy index was .759 and life expectancy at birth was 70.5 years. Georgia's GDP was $8.15 billion 
or $1,572 per capita, for a GDP index value of .675. The HDI in 1990 was .739, and the Shadow 
Economy equaled 45.1% of the total GDP. The GDP per capita hit its low in 1994, registering 
$458, and as of the 2008 data collection, had yet to rebound to meet levels prior to the USSR 
breakup. The average annual growth rate in GDP per capita from 1990 to 2008 was -.012%. 

In 2008, the population of Georgia decreased to 4.31 million. On average, 99.0% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 76.7%. The education index was .916; 10.1 1% of Georgia's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .777 with expectancy of 71.6 
years. GDP Index was .641, GDP decreased to $5.47 billion and $1,249 per capita. The HDI in- 
creased to .778 or 89th in the world. Georgia's estimated underground economy from 1999 to 
2008 is the highest in the world, averaging 68.8%, which translates to $3.76 billion in 2008, 
bringing the gross GDP to $9.24 billion and income per capita, $2,108 per person. 



159 



Group #2 - Non-USSR Soviet Influenced States 

Bulgaria 

Bulgaria first won its independence in 1908. Bulgaria sided with the Axis powers during 
World War 11 and "communism emerged as the dominant political force" in 1944 (DOS, 2010, p. 
History). Due to the "loss of the Soviet market. . ., the standard of living fell by about 40%. [By] 
October 2002, the European Commission declared Bulgaria had a 'Functioning Market Econo- 
my'" (p. Economy). 

In 1990, Bulgaria's population was 8.72 million, 93.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 72.6%. Government expenditures on education 
were 8.54%. The education index was 2.42. The Life Expectancy index was .770 and life expec- 
tancy at birth was 71.2 years. Bulgaria's GDP was $14.56 billion or $1,671 per capita, for a GDP 
index value of .732. The HDI in 1990 was .803. GDP per capita hit its low in 1997, registering 
$1,373, and rebounded in 2003 to exceed levels prior to the USSR breakup. 

By 2008, the population of Bulgaria fell to 7.62 million. On average, 98.3% of those ag- 
es 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
82.4. The education index was .930; 9.26% of Bulgaria's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .802 with expectancy of 73. 1 years. GDP In- 
dex was .788, with GDP at $20.28 billion and $2,661 per capita. The HDI increased to .788 or 
61st in the world. Bulgaria's estimated underground economy from 1999 to 2007 averaged 
37.5%, which translates to $7.6 billion in 2007, bringing the gross GDP to $27.9 billion and in- 
come per capita, $3,659 per person, an annual average of 2.4%. 

Germany 

According to DOS, the area around today's Germany was, until 1871, "Europe's Ger- 
man-speaking territories. . .divided into hundreds of kingdoms, principalities, duchies, bishoprics, 
fiefdoms and independent cities and towns. In 962, the territories were part of the Holy Roman 

160 



Empire until the Congress of Vienna in 1815, which created the German Confederation made up 
of 38 independent states. The German Empire began in 1871, because of the Franco-Prussian 
war, and ended with the Treaty of Versailles in 1919, with the loss of the Alsace territory to 
France. After World War II, Germany's western border was re-drawn partitioning traditionally 
Slavic territory to Poland in 1945. Soviet forces maintained its occupation including the western 
part of Germany starting from West Berlin (p. History). 

During the 1950s, East German citizens fled to the West by the millions. The Soviets 
made the internal German border increasingly tight. . . [and on] August 13, 1961 . . .began building 
a wall through the center of Berlin, slowing down the flood of refugees and dividing the city" (p. 
History). As the Soviet Union began to dissolve in 1989, "a growing flood of East Germans be- 
gan to take advantage of the end of border restrictions in Austria and Hungary. On November 9, 
1989, the wall was open to free travel (p. History). The economy in Germany increased by 126% 
from 1990 to 2007 yet remains plagued by high unemployment and high infrastructure costs in 
the western region (p. Economy). 

Germany's population was 79.43 million in 1990, with very high literacy 99%, and com- 
bined gross enrollment of 75.8%. Government expenditures on education were 9.2% of total 
government expenditures. The education index was 2.90. The Life Expectancy index was .842 
and life expectancy at birth was 75.5years. Germany's GDP was $1,543 billion or $19,428 per 
capita, for a GDP index value of .932. The HDI in 1990 was .896. GDP per capita hit its low in 
1993, registering $20,268, and rebounded in 1994 to exceed levels prior to the USSR breakup. 

In 2008, the population of Germany increased to 82.1 million. On average, 99% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
88.1%. The education index was .954; 10.1% of Germany's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .913 with expectancy of 79.8 years. GDP In- 
dex was .975, and increased to $2,097 billion and $25,547 per capita. The HDI increased to .947 

161 



or 22nd in the world. Germany's estimated underground economy from 1999 to 2008 averaged 
16.1%, which translates to $337 billion in 2008, bringing the gross GDP to $2,435 billion and in- 
come per capita, $29,660 per person. 
Former Czechoslovakia: Czech Republic, Slovakia 

According to DOS, the Czech and Slovak ethnic groups from the Hungarian Empire to- 
gether are considered the largest ethnic group in Southeastern Europe, and were identified by 
their religious affiliations, where up to 69% of whom where Roman Catholic depending on the 
area. Forty percent were atheist. The Jewish population was 120,000 in 1948. In 1989, 3,000 
Jews remained, the balance lost to concentration camps (p. People). The Slovaks came from the 
Great Moravian Empire, while the Czechs came from the Hapsburg Empire becoming one coun- 
try, Czechoslovakia, on October 28, 1918. Despite the ethnic and cultural differences, world 
leaders kept the two states together in the Pittsburg Agreement, in May 1918, signed by Czecho- 
slovakian Prime Minister Thomas Masaryk, in an attempt to bridge the educational and economic 
inequality between the two regions. Josip Broz Tito, a Catholic Priest, lead the church and its 
people to become a Nazi Puppet State (p. Slovak, History). Communism, by Tito's lead, entered 
Czechoslovakia through the medium of the Catholic Church. The Communist Party took over the 
country by force in February 1948. "The next four decades, communist ruled under Alexander 
Dubcek" (p. History). Dubcek was removed due to a sluggish economy, under the Warsaw Pact, 
in 1968 (p. Economy), however, it remained stagnate through the 1980s. The Velvet Revolution, 
protesting 250 human rights violations by the government, started the demise of the communist 
strong hold, which ended December 1989 (p. History). The two countries, led by Klaus for the 
Czechs and Merciar for the Slovak people, formally separated in 1993. 

Czech Republic 

According to Europa, the Republic of Czechoslovakia's geographic boundaries were cre- 
ated in October 1918 out of the Czech lands of the Austrian Empire and Slovakia, from the 

162 



Hungarian Empire. The two were again split on December 16, 1992 into separate sovereign na- 
tions. Czech Republic "aligned itself with the Soviet-led Eastern European bloc, joining the 
Council for Mutual Economic Assistance (CMEA) and the Warsaw Pact. [The government fol- 
lowed a rigid Stalinist pattern" (p. 3775). Economic recovery began in January of 1991, with 
large influx of funds from the IMF. By 1995, the economy was 96% off 1990 levels, due in part 
to the economic downturn in neighboring countries with dissolution of the Soviet economic sys- 
tem. Through 2008, the political situation remained consistent, with democratic elections in a 
market economic system (p. 3775). The Check Republic's Shadow Economy is 19.8. 

In 1990, Czech Republic's population was 10.36 million, 97.0% of those ages 15 and 
above were literate, with a combined gross enrollment of 71.7%. Of government expenditures, 
16.9% were dedicated to public education. The education index was 2.68. The Life Expectancy 
index was .785, and life expectancy at birth was 72.1 years. Czech Republic's GDP was $55.29 
billion or $5,336 per capita, for a GDP index value of .859. The HDI in 1990 was .847, and the 
Shadow Economy equaled 13.1% of the total GDP. The GDP per capita hit its low in 1993, reg- 
istering $4,710, and rebounded by 2000 to exceed levels prior to the USSR breakup. The average 
annual growth rate in GDP per capita from 1990 to 2008 was 2.2%. 

In 2008, the population of Czech Republic increased slightly to 10.42 million. On aver- 
age, 99.0% of those ages 15 and above were literate in the years from 1999-2007, with combined 
gross enrollment of 83.4%. The education index was .938; 9.77% of Czech Republic's 2000- 
2008 government expenditure was on education. The Life Expectancy Index was .856 with ex- 
pectancy of 76.4 years. GDP Index was .916, at $79.15 billion and $5,336 per capita. The HDI 
increased to .903 or 36th in the world. Czech Republic's estimated underground economy from 
1999 to 2008 averaged 19.8%, which translates to $15.67 billion in 2008, bringing the gross GDP 
up to $94.83 billion, or $9,097 per person. 



163 



Slovakia 

According to DOS, Slovakia is still economically challenged due in part to the depend- 
ence on exports of oil to the USSR during the Cold War at the expense of developing a 
diversified economy and world trading partners. Yugoslavia is surrounded on three sides by four 
Soviet states. Slovakia's wartime dependence on oil exports with the former USSR left little in- 
frastructure on which its economy could grow. During "1994 through 1998 period, due to the 
Crony Capitalism under Prime Minister Merciar, the economy struggled. . .with high government 
spending and borrowing" (p. Slovakia, Economy). In 2008, Slovakia depended on the EU for 
85% of its exports, with little infrastructure for sustainable economic (p. Economy). 

In 1990, Slovakia's population was 5.28 million, 97.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 71.7%. Of government expenditures, 16.9% were 
dedicated to public education. The education index was 2.72. The Life Expectancy index was 
.776, and life expectancy at birth was 71.6 years. Slovakia's GDP was $27.52 billion or $5,21 1 
per capita, for a GDP index value of .811. The HDI in 1990 was .827, and the Shadow Economy 
equaled 15.1% of the total GDP. The GDP per capita hit its low in 1993, registering $3,967, and 
rebounded by 2001 to exceed levels prior to the USSR breakup. The average annual growth rate 
in GDP per capita from 1989 to 2008 was 6.4%. 

In 2008, the population of Slovakia increased slightly to 5.41 million. On average, 
99.0% of those ages 15 and above were literate in the years from 1999-2007, with combined 
gross enrollment of 80.5%. The education index was .928; 1 1.5% of Slovakia's 2000-2008 gov- 
ernment expenditure was on education. The Life Expectancy Index was .827 with expectancy of 
74.6 years. GDP Index was .885, at $46.45 billion and $8,591 per capita. The HDI increased to 
.880 or 42nd in the world. Slovakia's estimated underground economy from 1999 to 2008 aver- 
aged 19.7%, which translates to $9.1 billion in 2008, bringing the gross GDP to $55.6 billion and 
income per capita, $10,284 per person. 

164 



The Balkans: Albania, Former Yugoslavia, and Hungary 

The Balkan countries that make up the southernmost peninsula in central Europe, include 
former Yugoslavia, Albania, Turkey, Cyprus, and Greece. Nine countries (as of 2007) have 
coastlines on this peninsula, used for trade and travel routes to most of Europe and Eurasia in- 
cluding the Soviet Empire. The nine countries are Croatia, Bosnia and Herzegovina, Slovenia, 
Albania, Greece, Turkey, Cyprus, Bulgaria, and Montenegro (Elisseeff, 1998). Part of the Mon- 
gol Empire until overtaken in the late 1300s by the Ottoman Empire that ruled until 1923, the 
countries in this region were influenced by the Soviet Empire, contiguous with the Soviet Empire, 
and were under communist rule during a portion of the period from the beginning from WWI to 
1990. Albania was the only Balkan country to be absorbed by the USSR (DOS, 2010, p. Bal- 
kans). Sachs & Warner (1995a, p. 5) include this region as Socialistic in a study economic 
policy. 

Albania 

According to the DOS, the Republic of Albania's geographic boundaries today, are those 
created in 1385 by the Ottoman Empire and confirmed in 1912 in the Vlore Proclamation declar- 
ing its independence. Albania maintained a "strict Stalinist philosophy" (p. Albania) through 
occupations by Italy and Germany during WWII, and withdrew from the Warsaw Pact in 1968 to 
isolate itself further from trade dependence on progressive nations (p. Albania). Albania "was the 
last of the Central and Eastern European Countries to embark on democratic and free market re- 
forms" (p. Albania) resulting in positive economic growth by the late 1990s. The economy still 
struggles with a negative trade balance, about 4:1; its economic health depends on tourism flows 
from neighboring countries and the economic health of the EU, its major trading partner, which 
purchases 79.2% of Albania's exports (p. Economy). In 2003, the opposing political parties en- 
tered into a joint pledge for democratic elections and economic reforms. According to the OCSE, 



165 



the 2003 and 2005 elections were improved over past elections, yet subject to voting and cam- 
paign fraud (p. Albania). 

In 1990, Albania's population was 3.29 million, 85% of those ages 15 and above were 
literate, with a combined gross enrollment of 68.8%. Of government expenditures, 10.87% were 
dedicated to public education. The education index was 2.41. The Life Expectancy index was 
.782, and life expectancy at birth was 71.9 years. Albania's GDP was $3.21 billion or $978 per 
capita, for a GDP index value of .620. The HDI in 1990 was .784, and the Shadow Economy 
equaled 31% of the total GDP. The GDP per capita hit its low in 1992, registering $644, and re- 
bounded by 1999 to exceed levels prior to the USSR breakup. The average annual growth rate in 
GDP per capita from 1990 to 2008 was 3.34%. 

In 2008, the population of Albania shrank to 3.14 million. On average, 99.0% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment ra- 
tio of 67.8%. The education index was .886; 8.43% of Albania's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .858 with expectancy of 76.5 
years. GDP Index was .710, at $5.73 Billion and $987 per person. The HDI increased to .818 or 
70th in the world. Albania's estimated underground economy from 1999 to 2008 averaged 
36.3%, which translates to $2.1 billion in 2008, or $7.82 billion and Income per capita, $1,825. 
Former Yugoslavia 

The Yugoslavia Country Brief (DOS, 2010) reports the following on the People and Po- 
litical Highlights page. The Ottoman and Hapsburg Empires ruled over the geographic region of 
today's Yugoslavia until 1878, "when the Congress of Berlin transferred administrative control to 
Austria-Hungary" (p. Political Highlights). The Treaty of Versailles granted the Kingdom of Yu- 
goslavia sovereignty, until the Axis powers occupied the region through WWII. "The end of the 
war saw the establishment of a Communist, federal Yugoslavia under the wartime leader, Josip 
Broz Tito. . .[creating] six republics within the Yugoslav federation" (p. Political Highlights). "In 

166 



1948, after Tito made several foreign policy decisions without consulting Moscow, Yugoslavia 
was expelled from the Soviet Bloc (p. Serbia). . ..Despite the appearance of a federal system of 
government in Yugoslavia, Serbian communists ruled Yugoslavia's political life for four dec- 
ades" (p. Serbia). Yugoslavia's internal conflicts since the late 1980's have cost the region in 
terms of economic growth, and the 1999 NATO bombings further devastated its economic infra- 
structure (p. Economy). Yugoslavia lies between the Adriatic Sea and three former Soviet states, 
Hungary, Romania, and Bulgaria. Five Yugoslav republics have coastlines on the Adriatic con- 
trolling trade traffic from the east to the USSR (p. Economy). Disaggregated data on the 
percentage of government expenditures dedicated to education were not available for Yugoslavia. 

Bosnia and Herzegovina 

According to DOS, after the demise of the Soviet Empire, "Bosnia's parliament declared 
the republic's independence on April 5, 1992. . ..Bosnia and Herzegovina remains one of the 
poorer countries in Europe" (p. Economy). Through 1995, the war turned into internal conflict 
between ethnic groups, damaged or destroyed much of the economic infrastructure killing thou- 
sands (p. Economy). EU troops remained deployed there through 2008, to aid progress toward 
transparency in politics and banking (p. History). 

In 1990, Bosnia and Herzegovina's population was 4.31 million, 92.7% of those ages 15 
and above were literate, with a combined gross enrollment of 65.8%. The education index was 
2.34. The Life Expectancy index was .696, and life expectancy at birth was 66.7 years. Bosnia 
and Herzegovina's GDP was $14.7 billion or $1,445 per capita, for a GDP index value of .753. 
The HDI in 1990 was .803, and the Shadow Economy equaled 28% of the total GDP. The GDP 
per capita hit its low in 1993, registering $388, and rebounded by 1994 to exceed levels prior to 
the USSR breakup. The average annual growth rate in GDP per capita from 1993 to 2008 was 
.94%. 



167 



In 2008, the population decreased to 3.77 million. On average, 96.7% of those ages 15 
and above were literate in the years from 1999-2007, with combined gross enrollment of 69.0%. 
The education index was .874; 15.60 % of Bosnia and Herzegovina's 2000-2008 government ex- 
penditure was on education. The Life Expectancy Index was .834 with expectancy of 75. 1 years. 
GDP Index was .726, at $8.38 billion and $2,162 per capita. The HDI increased to .812 or 76th 
in the world. The estimated underground economy, from 1999 to 2008, averaged 34.6%, which 
translates to $2.9 billion in 2008, bringing the gross GDP to $1 1.29 billion and income per capita, 
$2,910 per person. 

Serbia 

According to the DOS, Serbia, with its current geographic boundaries, became a princi- 
pality under Russian protection in 1829, and gained its first independence in 1878 at the Congress 
of Berlin. The communist party invaded Yugoslavia in 1941 , under the direction of Josip Broz 
Tito, creating a satellite state for the party and a strong economy, able to remain sovereign during 
the Cold War year. During the communist years, the Serbian region was a regional military and 
economic power. Serbia maintained its continuity with Montenegro as one state until October 
2006, with a peaceful split of the traditional geographic regions (p. Serbia). 

In 1990, Serbia's population was 7.59 million, 92.3% of those ages 15 and above were 
literate, with a combined gross enrollment of 65.8%. The education index was 2.34. The Life 
Expectancy index was .776, and life expectancy at birth was 71.6 years. Serbia's GDP was 
$10.95 billion or $1,445 per capita, for a GDP index value of .814. The HDI in 1990 was .797. 
The Shadow Economy equaled 23.6% of the total GDP. The GDP per capita hit its low in 1993, 
registering $650, and has not rebounded to levels prior to the USSR breakup as of the 2008 data- 
reporting period. The average annual growth rate in GDP per capita from 1990 to 2008 was - 
.007%. 



168 



In 2008, the population of Serbia increased to 9.15 million. From 1999-2007, 96.4% of 
those ages 15 and above were literate, with combined gross enrollment of 74.5%. The education 
index was .891; 1 1.09% of Serbia's 2000-2008 government expenditure was on education. The 
Life Expectancy Index was .816 with expectancy of 73.9 years. GDP Index was .773, at $1.19 
billion and $1,262 per capita. The HDI increased to .826 or 67th in the world. Serbia's estimated 
underground economy from 1999 to 2008 averaged 39.67%, which translates to $1.19 billion in 
2008, bringing the gross GDP to $4. 19 billion and income per capita, $1,763 per person. 

Montenegro 

Montenegro gained its independence as a principality at the Congress of Berlin in 1878. 
Montenegro was unified with Serbia while being occupied by Austrian forces until WWI. Mon- 
tenegro gained its independence from Serbia on June 3, 2006. The elections of 2006 were 
"generally in line with international standards" (DOS, 2010, p. Montenegro). With coastline on 
the Adriatic, agricultural trade and tourism have become primary economic industries (p. Monte- 
negro). 

In 1990, Montenegro's population was 590,000, 92.3% of those ages 15 and above was 
literate, with a combined gross enrollment of 65.8%. The Education Index was 2.34. The Life 
Expectancy index was .843, and life expectancy at birth was 75.6 years. Montenegro's GDP was 
$848 thousand or $1,445 per capita combined with Serbia, for a GDP index value of .753. The 
HDI in 1990 was .815, and the Shadow Economy equaled 23.6% of the total GDP. The GDP per 
capita hit its low in 1999, registering $1,449, as a sovereign nation, rebounding by 2003 to exceed 
levels prior to the USSR breakup. The GDP per capita average annual growth rate from 1997 to 
2008 was 2.1%. 

In 2008, the population of Montenegro grew to 620 thousand. On average, 96.4% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 74.5%. The education index was .891; 9.33% of Montenegro's 2000-2008 government 

169 



expenditure was on education. The Life Expectancy Index was .817 with expectancy of 74 years. 
GDP Index was .795, at $1.45 billion and $2,335 per capita. The HDI increased to .834 or 65th 
in the world. Montenegro's estimated underground economy from 1999 to 2008 averaged 
39.67%, which translates to $570 thousand in 2008, bringing the gross GDP to $2.03 billion and 
income per capita, $3,261 per person. 

Croatia 

In 1868, the Archduke of Hungary assumed control over Croatia, to protect it from Turk- 
ish control. Croatia enjoyed domestic autonomy until the end of WWI, when it joined the 
Kingdom of Yugoslavia. Internal conflicts from 1990 through 1999 cost Croatia heavily in the 
industrial sectors and crushed its tourism industry. The 1999 bombings by NATO forces further 
destroyed infrastructure and trade throughout Yugoslavia. Its GDP in 1993 was only 60% of its 
1989 level. The economic rebound was slow, due to "corruption, cronyism, and general lack of 
transparency..." (DOS, 2010, p. Economy). 

In 1990, Croatia's population was 4.78 million, 92.7% of those ages 15 and above were 
literate, with a combined gross enrollment of 65.3%. The education index was 2.34. The Life 
Expectancy index was .782, and life expectancy at birth was 71.9 years. Croatia's GDP was 
$25.1 billion or $5,285 per capita, for a GDP index value of .805. The HDI in 1990 was .817, 
and the Shadow Economy equaled 24.6% of the total GDP. The GDP per capita hit its low in 
1993, registering $3,469 and rebounded by 2002 to exceed levels prior to the USSR breakup. 
The average annual growth rate in GDP per capita from 1990 to 2008 was 1.3%. 

In 2008, the population of Croatia decreased slightly to 4.43 million. On average, 98.7% 
of those ages 15 and above were literate in the years from 1999-2007, with combined gross en- 
rollment of 77.2%. The education index was .916; 34.7% of Croatia's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .850 with expectancy of 76 years. 
GDP Index was .847, at $30.18 billion, the per capita figure to $6,707. The HDI increased to 

170 



.871 or 45th in the world. Croatia's estimated underground economy from 1999 to 2008 aver- 
aged 34.7%, which translates to $10.47 billion in 2008, bringing the gross GDP to $40.65 billion 
and income per capita, $9,168 per person. 

Macedonia 

The Treaty of Versailles laid out the geographic area of Macedonia, which partitioned 
parts of the country off to Bulgaria and Greece. Its constitution as an independent country took 
effect November 20, 1991. By 2008, Macedonia had met the criteria for membership to NATO. 
The economy is plagued with dated industrial infrastructure, and out-migration of the skilled la- 
bor. Civil war between ethnic Albanians in 2001 slowed economic progress (DOS, 2010, p. 
Macedonia). 

In 1990, Macedonia's population was 1.91 million, 92.7% of those ages 15 and above 
were literate, with a combined gross enrollment of 65.8%. The education index was 2.34. The 
Life Expectancy index was .773, and life expectancy at birth was 71.4 years. Macedonia's GDP 
was $3.93 billion or $1,919 per capita, for a GDP index value of .753. The HDI in 1990 was 
.782, and the Shadow Economy equaled 35.6 % of the total GDP. The GDP per capita hit its low 
in 1994, registering $1,578, and rebounded by 2006 to exceed levels prior to the USSR breakup. 
The average annual growth rate in GDP per capita from 1990 to 2008 was .062%. 

In 2008, the population of Macedonia was 2.04 million. On average, 97% of those ages 
15 and above were literate from 1999-2007, with combined gross enrollment of 70. 1%. The edu- 
cation index was .880; 15.16% of Macedonia's 2000-2008 government expenditure was on 
education. The Life Expectancy Index was .819 with expectancy of 74.1 years. GDP Index was 
.753, at $4.43 billion and $2,158 per capita. The HDI increased to .817 or 72 nd in the world. 
Macedonia's estimated underground economy from 1999 to 2008 averaged 36.2%, which trans- 
lates to $1.6 billion in 2008, bringing the gross GDP to $6.5 billion and income per capita, $2,940 
per person. 

171 



Slovenia 

According to DOS, part of the Hapsburg Empire until 1918, Slovenia joined other Slavic 
states to form Yugoslavia in 1929. Axis powers Hungary, Italy, and Germany occupied the area 
until Tito came into power after WWII ended. Under his rule, "Slovenia became Yugoslavia's 
most prosperous republic," and after his death, embraced political openness and democracy "un- 
precedented in the communist world" (p. History) 

In 1990, Slovenia's population was 1.99 million, 92.7% of those ages 15 and above were 
literate, with a combined gross enrollment of 72.7%. The education index was 2.34. The Life 
Expectancy index was .801, and life expectancy at birth was 73. 1 years. Slovenia's GDP was 
$16.61 billion or $8,317per capita, for a GDP index value of .854. The HDI in 1990 was .853, 
and the Shadow Economy equaled 22.9% of the total GDP. The GDP per capita hit its low in 
1992, registering $7,168, and rebounded by 1996 to exceed levels prior to the USSR breakup. 
The average annual growth rate in GDP per capita from 1990 to 2008 was 2.69%. 

In 2008, the population of Slovenia increased to 2.02 million. On average, 99.7% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 92.8%. The education index was .969; 12.59% of Slovenia's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .886 with expectancy of 78.2 
years. GDP Index was .933, at $16.61 billion and $13,789 per capita. The HDI increased to .929 
or 29th in the world. Slovenia's estimated underground economy from 1999 to 2008 averaged 
28%, which translates to $7.8 billion in 2008, bringing the gross GDP to $35.68 billion and in- 
come per capita, $17,650 per person. 

Hungary 

The Treaty of Trianon created the Hungarian borders in 1920, which divided the Austro- 
Hungarian Empire into the Czechoslovakia, Yugoslavia, Austria, Hungary, and small parts of Po- 
land (DOS, 2010, p. Hungary). The territory, excluding Austria, had remained constant from 895 

172 



to 1867. In 2007, 90% of Hungary's population was ethnic Myagyar (p. People). Hungary lost 
over 50% of its export market when the Soviet Union collapsed. It is plagued with corruption, 
and its state -owned industries cannot yet compete with free-market competitors (p. People). 

In 1990, the population was 10.37 million, 97% of those above 15 were literate. The 
combined gross enrollment was 67.7%. Government expenditures on education were 5.27%. 
The education index was 2.73. The Life Expectancy index was .740, life expectancy at birth was 
69.4 years. Hungary's GDP was $43.98 billion, $4,240 per capita. GDP index was .812. The 
HDI in 1990 was .812. The Shadow Economy equaled 22.3% of the total GDP. The GDP per 
capita low was $3,606 in 1993, rebounding in 1999 to exceed levels prior to the USSR breakup. 
The average growth rate in GDP per capita from 1990 to 2008 was 2.03%. 

In 2008, the population of Hungary had declined to 10.04 million. Average literacy of 
those 15 and above was 98.9% from 1999-2007, with combined gross enrollment of 90.2%. The 
education index was .960; 11.50% of Hungary's 2000-2008 government expenditure was on edu- 
cation. The Life Expectancy Index was .805. Life expectancy was 73.3 years. GDP Index was 
.874, with GDP at $62.39 billion and $6,216 per capita. The HDI increased to .879 or 43rd in the 
world. Hungary's estimated underground economy from 1999-2008 averaged 25.8%, which 
translates to $16.1 billion in 2008, bringing the gross GDP to $78.49 billion and income per capi- 
ta, $7,820 per person. 

Mongolia 

According to DOS, Chinggis (Genghis) Khan created "a single Mongolian state. . .based 
on nomadic tribal groupings" (p. People), in 1206. Mongolia's sovereignty from China depended 
on military assistance from the Russian and Soviet Empires 1727 through 1989. The government 
began its transition to a democracy after three centuries of communist rule in 2008. Mongolia is 
the second largest land-locked country creating Russia's Siberian border of over 3,400 kilome- 
ters. With vast mineral deposits and a well-developed agricultural sector, the economy started to 

173 



rebound after civil unrest during the 2006 attempt to convert to a transparent market economy (p. 
Economy). Mongolia is nearly ethnically homogeneous: 94.9% Mongol, 5% Kazakh (CIA, 2010, 
p. People). 

In 1990, Mongolia's population was 2.22 million, 93.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 65.5%. Of government expenditures, 17.96% were 
dedicated to public education. The education index was 2.42. The Life Expectancy index was 
.596 and life expectancy at birth was 60.8 years. Mongolia's GDP was $1.09 billion or $496 per 
capita, for a GDP index value of .35. The HDI in 1990 was .676. 

In 2008, the population of Mongolia increased to 2.64 million. On average, 97.3% of 
those ages 15 and above were literate in the years from 1999-2007, with combined gross enroll- 
ment of 79.2%. The education index was .914; 15.94% of Mongolia's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .687 with expectancy of 66.2 
years. GDP Index was .580. The GDP growth rate was 2.09% to $1.94 billion and $735 per 
capita. The HDI increased to .727 or 1 15th in the world. Mongolia's estimated underground 
economy from 1999 to 2008 averaged 37.9% (Zhou, 2007, p. 23), which translates to $736 mil- 
lion in 2008, bringing the gross GDP to $2.68 billion and income per capita, $1,014 per person. 



174 



Group #3 - Non-USSR Central and Eastern Europe 

Neighboring Socialist Countries: Austria, Cyprus, Finland, Greece, Italy, Turkey 

Austria 

According to DOS, The Treaty of St. Germain granted independence to the Austrian Re- 
public in 1919, after over 700 years in the Hapsburg and Austrian Empires. Austria was annexed 
by Germany in 1938. It was free and independent again on October 25, 1955, after the signing of 
the Austrian State Treaty (p. History). "Since 1955, Austria has enjoyed political stability" (p. 
Political Conditions). According to the WTO, as of 2007, Austria was actively part of trade 
agreements with ten other countries in the sample set of counties in this thesis (WTO, 2010g). 
Austria shares borders, over 95% of its borders, with former communist countries, and all of these 
are still socialist countries. 

In 1990, Austria's population was 7.71 million, 99.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 77.5%. Of government expenditures, 8.13% were 
dedicated to public education. The education index was 2.90. The Life Expectancy index was 
.844 and life expectancy at birth was 75.6 years. Austria's GDP was $149.0 billion or $19,324 
per capita, for a GDP index value of .936. The HDI in 1990 was .899. Austria's Shadow Econ- 
omy equaled 7.0% of the total GDP, the lowest percentage in the sample set. The GDP per capita 
hit its low in 1992, registering $19,861, and rebounded by 2004 to exceed levels prior to the 
USSR breakup. The average annual growth rate in GDP per capita from 1990-2008 was 1.8%. 

In 2008, the population of Austria increased to 8.34 million. On average, 99.0% of those 
ages 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
90.5%. The education index was .962; 11.17% of Austria's 2000-2008 government expenditure 
was on education. The Life Expectancy Index was .915 with expectancy of 79.9 years. GDP In- 
dex was .989, GDP rose to $227.1 billion and $27,251 per capita. The HDI increased to .955 or 
14th in the world. Austria's estimated underground economy from 1999-2008 averaged 14.6%, 

175 



which translates to billion in 2008, bringing the gross GDP to $260.4 billion and income per capi- 
ta, $3 1 ,229 per person. 

Greece 

The Roman Empire conquered Greece in 146 BC, in the Battle of Corinth, and Constan- 
tinople (Istanbul, Turkey) was the capital of the Roman Empire until 1453 (Robinson, 1902, p. 
356). Greece expanded its territory to include southern parts of present-day Macedonia, Albania, 
and Bulgaria (DOS, 2010, p. History). Greece entered WWII fighting alongside the Allies, and 
then was occupied by Hitler's forces from 1941-1944. With the aid of the 1947 Truman Doctrine 
that pledged US support for Turkey and Greece against Soviet threats, Greece did not become a 
communist country and it remains in the territory of ancient Greece (DOS, 2010, p. History). As 
of 2007, Greece exported over half of its total exported goods to countries in Central and Eastern 
Europe. Germany received 1 1 . 1 1 % of its total, and 1 1 .05% shipped to Italy, 7.28% shipped to 
Cyprus, and 6.74% to Bulgaria, and Turkey received 4.23%. Germany provided to Greece 
13.73% of its imports, Italy 12.71% (p. Economy). Greece's Panhellenic Socialist Movement 
(PASOK) prevailed in securing a socialistic government for Greece in 1981 and again in 2009 (p. 
Government). According to the WTO (2010h), as of 2007, Greece was actively part of trade 
agreements with eight members other counties in the SET. Greece is bounded substantially on its 
inland borders, with Macedonia and Bulgaria. 

In 1990, Greece's population was 10.16 million, 93.2% of those ages 15 and above were 
literate, with a combined gross enrollment of 77.4%. Of government expenditures, 6.95% were 
dedicated to public education. The education index was 2.41. The Life Expectancy index was 
.869, and life expectancy at birth was 77.2 years. Greece's GDP was $99.61 billion or $9,803 per 
capita, for a GDP index value of .872. The HDI in 1990 was also .872, and the Shadow Economy 
equaled 23.7% of the total GDP. The GDP per capita hit its low in 1993, registering $9,723, and 



176 



rebounded by 1995 to exceed levels prior to the USSR breakup. The average annual growth rate 
in GDP per capita from 1990-2008 was 2.3%. 

In 2008, the population of Greece grew to 11.24 million. On average, 97.1% of those ag- 
es 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
100% (p. Table Q). The education index was .981; 9.65% of Greece's 2000-2008 government 
expenditure was on education. The Life Expectancy Index was .902 with expectancy of 79.1 
years. GDP Index was .944, at $170.83 billion and $15,203. The HDI increased to .942 or 25th 
in the world. Greece's estimated underground economy from 1999 to 2008 averaged 29.9%, 
which translates to $51 billion in 2008, bringing the gross GDP to $221.92 billion and income per 
capita, $19,749 per person. 

Italy 

Italy was a Constitutional Monarchy from 1870 through 1922 after centuries of rule by 
the Holy Roman Empire. Italy joined the Triple Alliance with Austria-Hungary and Germany 
from 1882 through 1914. Italy entered WWI on the side of the Allies in an attempt to regain ter- 
ritory lost to Austria. Mussolini began his quest for leadership in 1917, after serving in the 
infantry in the war on Austria (DOS, 2010, p. People and History). "In 1922, Benito Mussolini 
came to power and, over the next few years, eliminated political parties, curtailed personal liber- 
ties, and installed a fascist dictatorship termed the Corporate State" (p. People and History), allied 
with Hitler. Italy remained under the control of the Fascist party until Mussolini's over throw in 
1943, then had its economic infrastructure decimated as the battlefield of the Italian Campaign (p. 
History). Before WWII, thousands of unemployed Italians went to work in Germany in factories 
and shops to back-fill the skill drain from German workers into the military. The Volkswagen 
plant alone employed fifteen hundred Italian men, who were then denied repatriation into Italy at 
the start of WWII, as the plant began producing armaments (Burleigh, 1996, p. 43). Italy has 
been a constitutional republic since 1942 (DOS, 2010, p. Government). Italy claimed itself a Pro- 

177 



tectorate State over Albania from June 23, 1917 until 1920, and over Montenegro from 1941- 
1943. As of 2008, Germany remained Italy's largest export market at 12.8% of its total. Germa- 
ny sends 16.0% and Russia 4.3% of Italy's total imports (p. Economy). According to the WTO 
(2010g), as of 2007, Italy was actively part of trade agreements with nine sample set counties in 
Central and Eastern Europe. Trieste was the southern city on the Adriatic of Churchill's Iron 
Curtain. Italy borders Slovenia and Austria on its inland border and is less than one hundred 
miles across the Adriatic Sea from six communist countries and trading partners: Croatia, Bosnia 
and Herzegovina, Montenegro, Macedonia, Albania, and Greece. 

In 1990, Italy's population was 56.72 million, 97.1% of those ages 15 and above were lit- 
erate. The combined gross enrollment of 77.8%. Of government expenditures, 9.64% were 
dedicated to public education. The education index was 2.54. The Life Expectancy index was 
.864. Life expectancy at birth was 76.9 years. Italy's GDP was $937.6 billion or $16,53 1 per 
capita, for a GDP index value of .923. The HDI in 1990 was .889. The Shadow Economy 
equaled 23.4% of the total GDP. The GDP per capita hit its low in 1993, at $16,730, and re- 
bounded by 1994 to exceed levels prior to the USSR breakup. The average annual growth rate in 
GDP per capita from 1990 to 2008 was .8%. 

In 2008, the population of Italy grew to 59.83 million. On average, 98.9% of those ages 
15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
91.8%. The education index was .965; 9.51% of Italy's 2000-2008 government expenditure was 
on education. The Life Expectancy Index was .935 with expectancy of 8 1 . 1 years. GDP Index 
was .954, at $1,171.8 billion and $19,586 per capita. The HDI increased to .951 or 18th in the 
world. Italy's estimated underground economy from 1999-2008 averaged 27.2%, or $318.7 bil- 
lion in 2008, bringing the gross GDP to $1,490.6 billion and income per capita, $24,914 per 
person. 



178 



Turkey 

The Republic of Turkey emerged in 1923 after the collapse of the Ottoman Empire. 
"Turkey entered WW II on the Allied side shortly before the war ended" for security from "de- 
mands by the Soviet Union for military bases in the Turkish Straits" (DOS, 2010, p. History). 
Turkey was not a communist country. Trade relations are strong with the Central and Easter Eu- 
ropean community, except for Armenia. Turkish forces took part in the blockade of Armenia's 
rail traffic in 1992, closing its borders to Armenia in 1993. As of 2009, Germany was the largest 
provider for the Turkish import market, at 9.6%, and Italy provided 5.8% of Turkey's imports. 
Russia was the largest export market at 14%; Germany was second at 10%, and Italy fifth largest 
at 5.4%. In 2005, Turkey opened its new oil and gas pipeline with Azerbaijan and Georgia to 
transport up to a million barrels of Caspian Sea oil a day (p. Economy). As of 2007, Turkey was 
actively part of trade agreements with ten SET countries (WTO, 2010g). 

In 1990, Turkey's population was 56.09 million, 80.7% of those ages 15 and above were 
literate, with a combined gross enrollment of 55.0%. Government expenditures on education 
were 15.55%. The education index was 1.82. The Life Expectancy index was .660 and life ex- 
pectancy at birth was 64.6 years. Turkey's GDP was $186.6 billion or $3,328 per capita, for a 
GDP index value of .742. The HDI in 1990 was .705, and the Shadow Economy equaled 20.5% 
of the total GDP (Yereli et al., 2007, p. 89). The GDP per capita hit its low in 1991, registering 
$3,293, and rebounded by 1992 to exceed levels prior to the USSR breakup. The average annual 
growth rate in GDP per capita from 1990 to 2008 was 2.2%. 

In 2008, the population of Turkey grew to 73.91 million. On average, 88.7% of those ag- 
es 15 and above were literate in the years from 1999-2007, with combined gross enrollment of 
71.1. The education index was .828; Turkey's 2000-2008 government expenditure on education 
was 14.74% of total government expenditures. The Life Expectancy Index was .779 with expec- 
tancy of 71.7 years. GDP Index was .812, GDP increased to $375 billion and $19,586 per capita. 

179 



The HDI increased to .806 or 79th in the world. Turkey's estimated underground economy from 
1999-2008 averaged 32.9%, which translates to $123 billion in 2008, bringing the gross GDP to 
$498.47 and income per capita, $6,744 per person. 

Cyprus 

Cyprus is located in the Mediterranean Sea, near the Balkan Peninsula, 47 miles south of 
Turkey. Cyprus was under the protection of Great Britain as a Crown Colony since 1878, after 
centuries in the Ottoman Empire. The Zurich and London Agreement between the United King- 
dom, Greece and Turkey on August 16, 1960. After a coup d' etat by the Greek Junta in 1974, it 
was taken by force by Turkish troops. As of 2006, Cyprus had a democratically elected com- 
munist government and it remains a trading partner with much of Europe. As of 2007, the largest 
export markets for Cyprus were the UK, Greece, and Russia. The largest import markets are 
Greece, Italy, and Germany (DOS, 2010, p. People and History). According to the U.S. Library 
of Congress (1991), in 1960, the census reported seventy-seven percent of the population of Cy- 
prus was Greek Cypriots, and twenty-three percent Turkish Cypriots. In the five years after the 
invasion by Turkish troops, 65,000 Cypriots emigrated, more than a third of whom went to 
Greece and Britain. Although the government declared itself a democratic republic in 1964, it 
remains Socialistic in character; as of "1986 fully 64% of the population [remains] living in gov- 
ernment-controlled areas of Cyprus (p. Population)," and high tariffs remained in place through 
the early 1990s to protect native industries (p. Economy). According to the WTO, as of 2007, 
Cyprus was actively part of trade agreements with ten members SET counties (WTO, 2010g). 

In 1990, Cyprus's population was .68 million, 87.0% of those ages 15 and above were 
literate, with a combined gross enrollment of 62.4%. Government expenditures on education 
were 13.17% of total government expenditures. The education index was 2.27. The Life Expec- 
tancy index was .859 and life expectancy at birth was 76.5 years. Cyprus's GDP was $6. 19 
billion or $10,684 per capita, for a GDP index value of .850. The HDI in 1990 was .849, and the 

180 



Shadow Economy equaled 21% of the total GDP. The GDP per capita hit its low in 1991, regis- 
tering $10,488, and rebounded by 1994 to exceed levels prior to the USSR breakup. The average 
annual growth rate in GDP per capita from 1990-2008 was 1.9%. 

In 2008, the population of Cyprus grew to .86 million. On average, 97.7% of those ages 
15 and above were literate from 1999-2007, with combined gross enrollment of 77.6. The educa- 
tion index was .910; 13.64% of Cyprus's 2000-2008 government expenditure was on education. 
The Life Expectancy Index was .910 with expectancy of 79.6 years. GDP Index was .920; GDP 
doubled to $12.3 billion and $15,510. The HDI increased to .914 or 32nd in the world. Cyprus's 
estimated underground economy from 1999 to 2008 averaged 29.4%, which translates to $3.6 bil- 
lion in 2008, bringing the gross GDP to $15.92 billion and income per capita, $20,069 per person. 

Finland 

The geography occupied by Finland today has been roughly the same for about 900 
years. From 1 154-1809 subject to the Kingdom of Sweden, as the Grand Duchy of Finland, sub- 
ject to the Russian Empire until 1917, and the Independent Finnish Republic since. However, 
Finland was a pawn in the Molotov-Ribbentrop non-aggression pact between Germany and the 
Soviet Union, falling under the "Soviet Sphere of Influence" forcing Finland to defend itself from 
the Soviet Union in the Winder War of 1939-1940 (DOS, 201 1, p. Finland, History). "During 
Continuation War (1941-1944), Finland was a co-belligerent with Germany" (p. History) against 
the Soviet Union. During the "Lapland War of 1944-1945, Finland fought against the Germans" 
(p. History). Finland was neutral after WWII, under the Finno-Soviet Pact of Friendship that 
lasted from 1948 to 1992 (p. History). 

In 1990, Finland's population was 4.99 million, with a combined gross enrollment of 
62.4%, and a 99.6% adult literacy rate. Government expenditures on education were 11.9% of to- 
tal government expenditures. The Education Index was 2.25. The Life Expectancy index was 
.759 and life expectancy at birth was 70.5 years. Finland's GDP was $99.3 billion, $19,916 per 

181 



capita. GDP index was .904. The HDI in 1990 was .959, and the Shadow Economy equaled 
14.5% of the total GDP. The GDP per capita hit its low in 1993, registering $17,638, and re- 
bounded by 1997 to exceed levels prior to the USSR breakup. GDP per capita average annual 
growth rate from 1990 to 2008 was 1.9%. 

In 2008, the population of Finland grew to 5.31 million. On average, 99% of those ages 
15 and above were literate in the years from 1999-2007, with combined gross enrollment of 77.6. 
The education index was .960; 12.59% of Finland's 2000-2008 government expenditure was on 
education. The Life Expectancy Index was .77 with expectancy of 71.6 years. GDP Index was 
.920; GDP increased to $153.77 billion and $28,941 per person. The HDI increased to .959. Fin- 
land's estimated average underground economy from 1999-2008 was 18.5%, which translates to 
$28.44 billion in 2008, bringing the gross GDP to $182.23 billion and income per capita, $34,295 
per person. 
Countries excluded from the Sample Set 

China 

China passes the first three rules of thumb: geographic, ethnolinguistic, and trade; but 
was communist with its own force of power. Unlike Mongolia, China operated a sovereign so- 
cialist state headed by Mao Zedong with the resources to maintain economic viability. "Outside 
the Soviet sphere of control. ... The dispute between militant China and the more moderate Sovi- 
et Union escalated into a schism in the world communist movement after 1960" (LOC, 201 la, p. 
Russia). 

Sweden, Switzerland, The Netherlands 

These countries pass the first three rules of thumb. According to the DOS (201 1), Swe- 
den was officially neutral during both WWI and WWII; it did support the war efforts on both 
sides with steel and "followed a policy of armed neutrality" during WWII (p. Sweden, History). 
Switzerland remained neutral during the 20 th century, though it did defend its own air space dur- 

182 



ing WWII (p. Switzerland, History). "The Netherlands proclaimed neutrality at the start of both 
world wars" (p. Netherlands, History). During WWI, however, it was used heavily for its trade 
routes. German forces invaded The Netherlands in 1940, which remained occupied until 1945, 
after which it became a founding member of NATO (p. History). 

Vietnam, Cuba, North Korea 

These countries are not included in the sample set. While politically and economically 
aligned with the former USSR, none pass the geographic rule of thumb. 



183 



Country Brief Summary 

The Sample Set of countries analyzed follows the grouping used by Schneider et al. 
(2010). However, the groupings between Central, Southern, and Eastern Europe, and Central and 
Western Asia vary greatly depending on the rise and decline of empires, armed conflict, and po- 
litical agenda. Countries of both the former Czechoslovakia and former Yugoslavia are included 
in the sample set, as disaggregated data are widely available, and the standard for cross-country 
analysis (HDR, 2009). The German Democratic Republic (GDR) is included the sample set in 
Group II, as post-German reunification data aggregate GDR with West Germany (FDR). Includ- 
ing Mongolia in the sample set captures the ethnolinguistic strength of the Mongolian Empire that 
began in centered in Mongolia and spread outward toward Central and Easter Europe and south 
and east toward today's China; the Mongolian emigration is significant in Central and Easter Eu- 
rope, while the Chinese emigration west is scant (DOS, 2010b, p. Mongolia). 

Group III is a challenge in the sample set. For all of the empirical data-driven reasons 
stated above, and these few to follow, this group of countries is substantially similar in that the 
end of the Cold War marked the end of an economic era, and an economic low point or trough. 
However, including these countries may add ambiguity to the results, or, it may make the results 
clearer. Countries that were influenced by the Cold War but not part of the former USSR or one 
of its satellite states may have had fewer of the symptoms of economic growth handcuffs endured 
by communistic countries. 

In Group III, Austria, Greece, Italy, Turkey, and Cyprus occupy the area of the Roman 
Empire, and Finland was part of the Sweden after about 800 and the Swedish Empire after 1611. 
Austria became part of the Hapsburg Empire and Greece, Turkey, Italy and Cyprus remained part 
of the Eastern Roman Empire until 1453. Through WWI, the region experiences successive wars. 
Austria was occupied by a communist regime for 20 years, and is included in the sample set. 
Greece was occupied for four years by a communist regime, is on the Balkan Peninsula, and has a 

184 



socialist government. Italian forces invaded Greece in the Greco-Italian War in 1940 (DOS, 
2010, p. Greece). Greece occupied parts of Albania, Macedonia, Bulgaria, and Turkey during its 
expansionary years between 1913 and 1923 (p. History). Italy allied itself with a communist re- 
gime for seventeen years, is geographically contiguous to two Socialist states, and is fewer than 
one hundred miles by sea from five more. Turkey is in the middle of the Balkan Peninsula and 
Cyprus is 47 miles south in the Mediterranean; each remain key trading partners and with former 
Soviet countries, and have strong geopolitical ties with Greece and Germany. Turkey occupied 
Cyprus in 1974, and has an elected communist government. 

As time goes on the hindsight becomes clearer, history will inform the community of re- 
searchers if this trough marked the start of a grand, 50 or 60-year period of "Take-off. . .when the 
old blocks and resistances to steady growth are finally overcome. The forces making for econom- 
ic progress . . . expand and come to dominate society. . ..Growth becomes normal, compound 
interest becomes built. . .into habits of institutional structure." On the other hand, was the country 
too suppressed that it was ready only to build the "pre-conditions for Take-Off, where a decisive 
feature is a new sense of nationalism, and major changes in social values and a shift in the econ- 
omy (Rostow, 1991, p. 7). Empirical data are clear; the economic depression hit every Central 
and Eastern European country, and many others around the world, while the economic dust set- 
tled along with that at the crumbled Berlin Wall. 

There are risks in including or excluding Groups II and III. Including both groups, the 
number in the sample set grows from fifteen to thirty-six countries. In Basic Economics, Gujarati 
& Porter (2009, p. 345) state, "sometimes, simply increasing the sample size (if possible) may at- 
tenuate the collinearity problem... the variance will decrease thus decreasing the standard error." 
The caution is to avoid "over- fitting" the model just to increase the goodness of fit, R 2 , because 
the addition of unnecessary variables will lead to a loss in the efficiency of the estimators and 
may also lead to the problem of multicolinearity, not to mention a loss in degrees of freedom" (p. 

185 



474). Given that the countries in Groups II and III exhibit the symptoms of an economic depres- 
sion (Burns & Mitchell, 1946) at the end of the Cold War and are Socialist by this thesis' 
standards, they belong in the sample group. Given that these same countries endured extraordi- 
nary influence by the Soviet Empire during the Cold War, and share borders, and to some degree, 
nationalities, religions, heritages, cultures, and histories, they should be included in the sample 
group. Lastly, since these countries are now, are again, or have continued to be trading partners 
and therefore, are linked economically, these counties belong in the sample group. Under-fitting 
the model, omitting a relevant variable, essentially trades less precision for greater bias (p. 473). 
"Thus, if economic theory says that [Groups II and III] . . .should both be included in the model 
explaining the. . .[change in income], dropping [one or the other Group] would constitute specifi- 
cation bias (p. 344)." 



186 



APPENDIX B: DATA RELIABILITY AND VALIDITY 

The overarching theory employed herein is Romer's New Growth Theory (NGT), which 
contends that both endogenous and exogenous factors influence growth of an economy. Reliabil- 
ity of the data and validity of the construct confirm that the data gathered 'hit the bull's eye' of 
the research object, or the research goal. Romer (1994b, p. 21) suggests important policy impli- 
cations of this theory. In addition to Romer (1986, 1990, 1994b, 1998b, 2007), the work of many 
scholars supports NGT. Recall that exogenous sources include among other factors, exogenous 
technological advancement, and knowledge spillovers from outside (Solow, 1957; North, 1994), 
and radical shaping events (e.g., armed conflict, regime change, famine) (Rostow, 1991). Endog- 
enous sources of growth include inertia (North, 1991a; Cortright, 2001), technology diffusion and 
adoption rates (Nelson et al., 1966, p. 71). Education (Sen, 1997; Barro et al., 2001), specializa- 
tion, and knowledge externalities are endogenous with increasing returns (Arrow, 1962; North, 
1991a; Phelps & Nelson, 1995). Finally, the national-level government (Galbraith, 1951; 
Rostow, 1991; Barro, 2001a), governance, institutions, and the policies set for national-level 
budgets and factors of development, education, and control over corruption are endogenous fac- 
tors (Rose-Ackerman, 1978; Klitgaard, 1988; deLeon, 1993; Mauro, 1995; Johnston, 2005; 
Kaufmann, 2006). The major source of data is the Human Development Reports. Before we 
begin describing the data analysis method, we must test the data reliability, and the validity of 
both the construct and of the SET data. 
HDR data for the Central and Eastern European SET 

From the start of the Human Development project in 1990, the Human Development Re- 
port for years 1990 to 2007 includes only that data which are deemed reliable, timely, verifiable 
and provided by "leading international data agencies at the time" (e.g., United Nations, IMF, 
World Bank, EuroStat, UNESCO, WHO, OECD) (2008c, p. 225). Therefore, as iterative re- 
search uncovers new data, more countries are added, more indicators are added to the total 

187 



report, and more data points are added to that years' HDI, making it increasingly more reliable 
over time (HDR, 1990, pp. 111-1 12, 2008c, pp. 223-227). While the underlying data was becom- 
ing more robust, the relativity of the component indices (used herein) has remained constant. In 
several circumstances, the HDR adopted a new methodology and provided tables reflecting the 
old and new data (p. 227). 
Test Equation 1.1: HDI Data Reliability 

First, we will test the reliability of the Human Development Index in 1990 (HDI1990) for 
the SET countries versus the population of countries, following Gujarati & Porter (2009). We 
will run a linear regression using the HDI component indices as independent variables, predicting 
the overall HDI1990 on SET. We will do the same for the HDI 2 oo7- 

Null Hypothesis: H : HDI j 990 ^ GDPI 19 9 + LET 990 + EAT 990 
Maintained Hypothesis: H^ HDI1990 = GDPI1990 + LEI1990 + EAI1990 
Test Equation 1.2: HDI Data Reliability 

Null Hypothesis: H : HDI2007 ^ GDPI2007 + LEI2007 + EAI2007 
Maintained Hypothesis: H^ HDI2007 = GDPI2007 + LEI2007 + EAI 2007 
If the SET data yield statistically significant and similar results to the population set 
(POP), then the SET data are reliable, and the SET data reflects the variations identified in POP. 

If the variation in the change in HDI from 1990 to 2007 is largely explained by the 
change in the HDI component indices from 1990 to 2007, my research construct was invalid, and 
we need not explain the any balance of variation with other variables (Weimer, 1998). In other 
words, if the 1990 to 2007 change in the GDPI, the LEI, and EAI, equally weighted, offer suffi- 
cient explanatory power of the change in the HDI for the SET, then we needn't look to corruption 
or education spending to further explain the HDI change in the SET. If the change in HDI does 
not equal the change in the component indices, then we will reject the null hypothesis and accept 



for now that at least one other variable explains the variation in the change in HDI over our date 
range. We will run a linear regression for this test. 
Test Equation 2. 1 : HDI Construct Validity Testing 

Null Hypothesis: H : AHDI = AGDP1+ ALEI+ A EA1 

Maintained Hypothesis: H t : AHDI ^ AGDPI+ ALEI+ AEAI 

Lastly, we will run a linear regression using the Ale as the dependent variable, and the 
AHDI to make sure that the construct remains valid when testing the relative change, adding con- 
fidence to the internal validity of the data (Gujarati & Porter, 2009). 
Test Equation 2.2: HDI Construct Validity Testing 

Null Hypothesis: H : Ale = AHDI 

Maintained Hypothesis: Hi: Ale ^ AHDI 

If we reject the null hypotheses, then we can conclude for now that the variation in the 
Ale from 1990 to 2007 cannot be explained by the AHDI alone. Assuming the null hypotheses 
are rejected, this concludes the construct validity testing for the HDR variables. From this point, 
we can test the HDI data being confident that SET represents the population, and that the test 
methods are generalizable for the entire population set, and the data are externally valid. 
Shadow Economy Data for the Central and Eastern Europe SET 

Schneider, 2010, provides the statistics on the SE data. To test the reliability of Sample 
SET countries, we will run a paired-t test to compare the average SE for each year for the SET to 
the average for each year of the Population (POP) set (pp. 44-45). 
Test Equation 3.1: SE Data Reliability 

Null Hypothesis: H : Mean Yearly Ave of Set ^ Mean Yearly Ave of SET 

Maintained Hypothesis: Hi : Mean Yearly Ave of Set = Mean Yearly Ave of SET 

If we reject the null hypothesis, we can conclude temporarily that SET average represents 
the POP average for each year of the study at 95% confidence level. To test the validity of the 

189 



construct, we will use a paired-t test to test the average SE on the SET countries using the two 
sets of sampling specifications found in Schneider, 2010, looking for equality in the averages. 
The study employs' a "Multiple Indicators Multiple Causes (MIMIC) model - a particular type of 
a structural equations model (SEM) - to analyze and estimate the shadow economies of 162 
countries around the world (p. 10). 
Test Equation 3.2: SE Data Reliability and Validity 

Null Hypothesis: 

H : Mean Ave SE / Country M1M1C6 ± Mean Ave SE / Country MIMIC7 

Maintained Hypothesis: 

H! : Mean Ave SE / Country MIMIC6 = Mean Ave SE / Country MIMIC7 

If we reject the null hypothesis, we can conclude temporarily (given both the MIMIC6 
and MIMIC7 data sets are reliable for the population of countries (p. 17)) that the SET data are 
valid, and we can test the size of the Shadow Economy as specified in the models used by 
Schneider. 
Educational Expenditure data for the SET 

To test the reliability of the SET of EE data, we start with the education data set for the 
entire population of countries, EdStats, provided by UNESCO (United Nations Education, Sci- 
ence and Culture Organization) and the data methodology from the technical reference manual 
(1998). EdStats Reports for years 1970 to 2007 includes only that data which are deemed relia- 
ble, timely, verifiable and follow the Statistical Information System on Expenditure in Education 
(SISEE) methodology protocols (1998). Therefore, as iterative research uncovers new data, more 
countries are added, more indicators are added to the total report, and more data points are added 
to that years' statistics, making it increasingly more reliable over time. 

In 1970, UNICEF gathered EE data on 71 countries data, 102 countries in 1975, and for 
the decade of the 1970's, UNICEF gathered data on 143 countries and 263 data points. Only 13 

190 



of those data points were from SET; too scant for statistical reliability. From 1980 to 1988, 
UNICEF gathered data on 152 countries and 294 data points, 19 countries from the SET reported 
39 data points. From 1998 to 2007, UNESCO gathered 156 country reports on 785 EE data 
points, 29 countries from the SET reported 172 data points. The pre-test EE uses the average EE 
from 1980 to 1988, and the post-test EE uses the average EE from 1998 to 2007. 

To test the data validity of the SET, we will use the un-paired-t test for both the pre-test 
and post-test data. If we reject the null hypothesis, we can conclude for now that the SET for EE 
data is reliable and valid, and we can proceed with testing our theory. 
Test Equation 4: EE Data Validity 

Null Hypothesis: H : Mean Ave EE of Data Set ^ Mean Ave EE Sample SET 
Maintained: Hj: Mean Ave EE of Data Set = Mean Ave EE Sample SET 
Assuming that the data are reliable, and the tests for construct validity confirm that the 
data gathered allow me to test my research object, then the construct validity is finished and we 
can move on to validating the data 
New Variables 

The last step in defining the data is to create new variables by including the factor of the 
SE on variables calculated using GDP. Recall, that subscript 1(0 denotes an Official figure as 
stated by the country in National Income Accounting, subscript 2 ( 2 ) denotes the unofficial fig- 
ures (Schneider et al., 2010b), and subscript 3(3) denotes the sum of official and unofficial figures 
(own calculations). In addition, the subscript for the year may be necessary on some data. Total 
GDP = GDP 3 , the Shadow Economy's portion of the GDP = GDP 2 , and the Official GDP = 
GDP . The equation, then, is GDPi + GDP 2 = GDP 3 , and Ici + Ic 2 = IC3. Each of the component 
measures of the HDI that include or are derived from the official GDP must be enhanced to in- 
clude the effects of that GDP in the Shadow Economy. 



191 



For example, the Official GDPi 990 in Ukraine was $243.35 billion in US equivalent dol- 
lars. The 16.3% SE 1990 was $39.66 billion, for a Total GDP 1990 of $283.01 billion. The equation 
for Ic 3 jggo is $4,716 Icj 1990 + $769 Ic 2 1990 = $5,485 in equivalent US dollars per capita. Stated 
otherwise, the official data report that the Ukrainian people earn $4,716 US equivalent dollars per 
person, but the SE makes up 16.3% of the total economy. Therefore, Ukrainians actually earn 
$5,485 US equivalent dollars on average. 

The second set of variables is the education funds that are a percentage of GDPi. The of- 
ficial Education Expenditure = EE^ The SE effective reduction of EE = EE 2 . Therefore, EE's 
percentage of actual Government Expenditures = EE 3 . 

Example: EEi Ukraine + EE 2 Ukraine = EE 3 Ukraine 

15.95% ,of GE + - (16.3% SE x 15.95% GE) 2 = 13.7145 3 % of GE 
Alternate Equation, in US $Billions: 

15.95% of GE x $243.35 GDP! _ $3,881.43 GEi _ $3,881.43 GEi 

$243.35 GDPi+ (16.3% x$243.35 GDPi) $243.35 GDP 1 +$39.661 GDP 2 ~~ $283.01 GDP 3 ~~ 

13.7148% of GDP 3 Expenditures 

Otherwise stated, the official figure for EE is 15.95% of government spending. However, 
since the SE effectively keeps 16.3% of the potential government revenue out of the government 
budgets, the official number is overstated, and education actually realizes, all else equal, 
13.7148% of the Government Expenditure budget. The SE is keeping for its use about $750 per 
Ukrainian citizen per year, given the policy is to invest 15.95% of its expenditure budget into 
public education. 



192 



Data and Construct Reliability and Validity Test Analysis 
Table 7.4 Correlation Coefficient Matrix. 

Correlate the change in Total Income per Capita, change in Total Education Expendi- 
tures, pre -test Human Development Index, pre -test Shadow Economy, change in Life Expectancy 
Index, change in Education Attainment Index, post-test Shadow Economy, change in Shadow 
Economy, Country Group. 
Test: Correlate $AIc3, $AEEc3, HDI 1990 , SE 200 8, ALEI, AEAI, SE 1990 , ASE, Group, (obs=36) 

Variable $AIc3 $AEEc3 HDI 1990 SE 2008 ALEI AEAI SE 1990 ASE Group 

$AIc3 1.0000 

$AEEc3 0.8619 1.0000 

HDI 1990 0.7710 0.5549 1.0000 

SE 200 8 -0.6876 -0.5285 -0.6040 1.0000 

ALEI 0.0807 0.1046 -0.1254 -0.1996 1.0000 

AEAI 0.2757 0.2394 0.4848 -0.2350 -0.0567 1.0000 

SE 1990 -0.5863 -0.5108 -0.4409 0.8660 -0.0844 -0.2308 1.0000 

ASE-0.1260 0.0618 -0.3071 0.1641 -0.1256 0.0313 -0.3163 1.0000 

Group 0.6549 0.5661 0.5648 -0.6958 0.3222 -0.0434 -0.6077 -0.0898 1.0000 

A linear regression tested data reliability of the Central and Eastern Europe sample set 

(SET) from HDIi 990 as the dependent variable, versus the entire data set of countries. The HDI 

component indices are the independent variables, predicting the overall HDI 1990 on the sample 

countries. 



193 



Test Equation 1.1: HDI Data Reliability 

Null Hypothesis: H () : HDI 1990 ^ GDPI 1990 + LEI 1990 + EAI 1990 
Maintained Hypothesis: H^ HDI 1990 = GDPI 1990 + LEI 1990 + EAI 1990 

Test: Linear Regression95% Confidence Level 

Regress dependent variable HDI1990 against independent variables GDPI1990, LEI1990, and 
EAI1990 

Source | SS df MS Number of obs = 34 
+ F( 3/ 3 ) = 119.18 

Model | .124129608 3 .041376536 Prob > F = 0.0000 

Residual | .010415456 30 .000347182 R-squared = 0.9226 

+ Ad j R-squared = 0.9148 

Total | .134545064 33 .004077123 Root MSE = .01863 

HDI1990 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

GDPI1990 | .2874717 .0488198 5.89 0.000 .1877685 .3871749 

LEI1990 | .3869957 .0748015 5.17 0.000 .2342306 .5397607 

EAI1990 | .0525218 .0176434 2.98 0.006 .0164893 .0885544 

_cons | .1577265 .0490298 3.22 0.003 .0575943 .2578586 

The regression showed with 95% certainty that variation in the HDI component indices 
for the sample set explained 91.48% of the variation HDIi 990 . Each of the independent variables 
proved to be significant, as well, with a high F-score on 33 degrees of freedom. Gujarati, et al. 
(2009) provides the following "2-f Rule of Thumb." "If the Number of degrees of freedom is 20 
or more, and if a, the level of significance, is set at .05, then the null hypothesis [P2= or ^ 0] can 
be rejected if the t value. . . exceeds 2 in absolute value". Under this rule, the null hypothesis is 
rejected. "[T]he /7-value is defined as the lowest significance level at which a null hypothesis can 
be rejected" (p. 122). The highest /7-value is for EAI, .006, or EAI would be rejected at a 99.95% 
confidence level. The high F-score and high adjusted-/? 2 scores are mostly a product of the inde- 
pendent variables being components of the HDI. The rejected null hypothesis allows for 
temporarily concluding that HDI 1990 components of the sample set are a reliable predictor of the 
composite of the sample set HDIi 990 . The same regression for the HDI2007 tested the 2007 data. 



194 



Test Equation 1 .2 

Null Hypothesis: H : HDI2007 i 1 GDPI2007 + LEI2007 + EAI2007 
Maintained Hypothesis: H^ HDI2007 = GDPI2007 + LEI2007 + EAI2007 

Test: Linear Regression95% Confidence Level 

Regress dependent variable HDI2007 using independent variables GDPI2007, LEI2007, and EAL.007 

Source | SS df MS Number of obs = 35 
+ F( 2, 31) =44499.97 

Model I .182026005 3 .060675335 Prob > F = 0.0000 

Residual | .000042268 31 1.3635e-06 R-squared = 0.9998 

+ Ad j R-squared = 0.9997 

Total | .182068273 34 .005354949 Root MSE = .00117 

HDI2007 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

GDPI2007 | .3358158 .0025453 131.93 0.000 .3306245 .3410071 

LEI2007 | .3296197 .00406 81.19 0.000 .3213392 .3379002 

EAI2007 | .3338652 .0067689 49.32 0.000 .3200599 .3476706 

_cons | .0001469 .0064856 0.02 0.982 -.0130806 .0133744 

The results of this 2007 test are more significant, as the data are a product of 16 iterations 
of HDI Reports, and with more data points on more countries by the international research agen- 
cies. The three component indices on 36 countries in the Central and Eastern Europe are each 
significant past the 99.99% level, and the overall regression describes 99.97% of the variation in 
the HDI 2 oo7- The rejected null hypothesis allows for temporarily concluding that the components 
contribute to the variance in the composite, and the data in the sample set are reliable. 

Wainer, et al. 1998 (in Golafshani) ask to identify validity, whether the data and research 
instrument allow the researcher to '"hit the bull's eye' of the research object" (2003. p. 2). The 
'object' for this thesis being a sample set useful for testing corruption's effects on education 
budgets and individual income. In the prior two tests the data are, with 95% certainty or greater, 
valid and reliable for the sample set in 1990 HDI. For the 2007 data sample set, the HDI compo- 
nents were much closer to the 'bull's eye' predicting the overall HDI2007 at over 99.9% level of 
certainty. 



195 



Construct Validity Testing 

A linear regression tests whether the AHDI does not equal the change in the component 
indices, a rejected the null hypothesis allows for temporarily accepting that at least one other var- 
iable explains the variation in the AHDI over the date range. 
Test Equation 2. 1 : HDI Construct Validity Testing 

Null Hypothesis: H : AHDI = AGDP1+ ALE1+ A EA1 

Maintained Hypothesis: H^ AHDI ^ AGDP1+ ALE1+ A EA1 

Test: Linear Regression 95% Confidence Level 

Regress dependent variable AHDI using independent variables AGDPI, ALEI, and AEAI 

Source | SS df MS Number of obs = 34 
+ F( 3/ 3 ) = 7 _23 

Model | .013050195 3 .004350065 Prob > F = 0.0009 

Residual | .018060088 30 .000602003 R-squared = 0.4195 

+ Ad j R-squared = 0.3614 

Total | .031110282 33 .000942736 Root MSE = .02454 

AHDI | Coef. Std. Err. t P> I t | [95% Conf. Interval] 
+ 

AGDPI | .1223597 .0547086 2.24 0.033 .0106298 .2340895 

ALEI | .2706155 .0781373 3.46 0.002 .1110379 .4301931 

AEAI | .0006193 .0005106 1.21 0.235 -.0004234 .0016621 

_cons | .0340377 .0065947 5.16 0.000 .0205696 .0475058 

The data show that with 95% certainty, 36% of the variation in the change in the HDI 
from 1990 to 2007 is attributable to the change in the equally weighted component indices. Of 
these results, the change in the Educational Attainment Index (EAI) produced the least significant 
result. This result suggests that the effect of the EAI on the variation in the HDI for this sample 
of countries is unsure. This result also supports testing alternative education measures that may 
add explanatory power to the equation. 

To test whether Ic is a sufficient proxy for individual human development, as part of the 
construct-validity testing with the sample data, a linear regression analysis substituting Ale as the 
dependent variable, and the AHDI as the independent variable. 



196 



Test Equation 2.2: HDI Construct Validity Testing 

Null Hypothesis: H : Ale = AHDI 

Maintained Hypothesis: Hi : Ale ^ AHDI 
Test: Linear Regression95% Confidence Level 
Regress dependent variable Ale with independent variable AHDI 

Source | SS df MS Number of obs = 32 
+ F( lf 3 ) = 8.19 

Model | 23.1670932 1 23.1670932 Prob > F = 0.0076 

Residual | 84.9092157 30 2.83030719 R-squared = 0.2144 

+ Ad j R-squared = 0.1882 

Total I 108.076309 31 3.48633254 Root MSE = 1.6824 

Ale | Coef. Std. Err. t P> I t | [95% Conf. Interval] 
+ 

AHDI | 31.72859 11.09 2.86 0.008 9.079775 54.3774 
_cons | .8248046 .6674307 1.24 0.226 -.5382707 2.18788 

A rejected null hypothesis allows us to conclude temporarily that the variation in Ale 
from 1990 to 2007 cannot be explained by AHDI from 1990 to 2007 alone. This result concludes 
the construct validity testing. With a f-value of 2.86, an F-score of 8.19 and 3 1 degrees of free- 
dom, this test passes the 2-t Rule of Thumb. Now, we can test the HDI sample data confident that 
the SET represents the population, the test methods are generalizable for the population of coun- 
tries, and the data are externally valid. 



197 



Shadow Economy Data for the Central and Eastern European SET 

To test the reliability of the SET, we will run a paired f-test to compare the average SE 

for each year for the Sample Set to the average for each year of the Set. 

Test Equation 3.1: SE Data Reliability 

Null Hypothesis: H : Mean Yearly Ave of SE Set = Mean Ave of SE SET 
Maintained Hypothesis: Hi : Mean Yearly Ave of Set ^ Mean Ave of SET 

Test: Two-Sample Unpaired t-test 99.9% Confidence Level 

Compare the Mean of the SE Set to SE SET. 

Variable | Obs Mean Std. Err. Std. Dev. [99.9% Conf. Interval] 

+ 

AVE70SETI 29 12.94213 .7020142 3.780462 10.363 15.52127 
AVE07set| 156 15.25357 .3839153 4.7951 13.96576 16.54138 

+ 

combined | 185 14.89124 .3468313 4.717416 13.73137 16.05111 

+ 

diff | -2.311439 .9411754 -5.459185 .8363066 

diff = mean (AVE07SET) - mean (AVE07Set) t = -2.4559 

Ho: diff = degrees of freedom = 183 

Ha: diff < Ha: diff != Ha: diff > 

Pr(T < t) = 0.0075 Pr(|T| > |t|) = 0.0150 Pr (T > t) = 0.9925 

In this case, the null hypothesis is maintained. The report above maintain that there is a 
.992% probability that the true average of the Set is greater than the average of the Sample Set, 
yet still remains in the acceptance area for the null hypothesis, we can conclude temporarily that 
the sample set average represents the POP average for each at 99.9% confidence level. 

To test the validity of the construct, an un-paired f-test compares the average SE on the 
same countries on the same years using the two sets of sampling specifications found in Schnei- 
der, 2010, looking for equality in the averages between two sets of specifications. 



198 



Test Equation 3.2: SE Data Reliability and Validity 

Null Hypothesis: 

H : Mean Ave SE / Country M1M1C6 ± Mean Ave SE / Country MIMIC7 

Maintained Hypothesis: 

H[ : Mean Ave SE / Country MIMIC6 = Mean Ave SE / Country MIMIC7 
Test: Two-Sample Paired Mest95% Confidence Level 
Compare the Mean of the Ave SE MIMIC6 to Mean Ave SE MIMIC7 set. 
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] 



+ 

MIMIC7 | 
MIMIC6 | 


23 
23 


33.56957 
38.67391 


2.517664 
2.421586 


12 .07429 
11.61352 


28 .34825 
33.65185 


38.79088 
43.69598 


diff I 


23 


-5.104348 


1.050569 


5.038351 


-7.283094 


-2.925602 



mean (diff) = mean (MIMIC7 - MIMIC6) t = -4.8587 

Ho: mean (diff) = degrees of freedom = 22 

Ha: mean(diff) < Ha: mean (diff ) != Ha: mean (diff ) > 

Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0001 Pr (T > t) = 1.0000 

In the case above, the null hypothesis is maintained. The report above maintains that 
there is a 1 % probability that the true average of the MIMIC6 of the Sample Set is greater than 
the average of MIMIC7 of the Sample Set, well past the rejection area for the null hypothesis. 
The temporary conclusion is that the Sample Set average MIMIC6 is lower than the Sample Set 
Average for MIMIC7 (given both the MIMIC6 and MIMIC7 data sets are reliable for the popula- 
tion (p. 17)). Further, that the sample set of data are valid, and test the size of the SE as specified 
in the models used by Schneider. 

To test the construct that the SE affects the Ale, the linear regression sets Ale as the de- 
pendent variable and the pre and posttest SE as the independent variable. 
Test Equation 3.3: Shadow Economy variables. 

Null Hypothesis: H : Ale = SE 1990 + SE 2 oo8 

Maintained Hypothesis: Hj : Ale ^ SE 1990 + SE 2 oo8 



199 



Test: Linear Regression95% Confidence Level 

Regress dependent variable Ale with independent variables SE 1990 and SE 2 oo8 

Source | SS df MS Number of obs = 36 
+ F( 2 , 33) = 14.82 

Model | 157229888 2 78614943.8 Prob > F = 0.0000 

Residual I 175083260 33 5305553.32 R-squared = 0.4731 

+ Ad j R-squared = 0.4412 

Total | 332313147 35 9494661.35 Root MSE = 2303.4 

Dch3lc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

SE90 | 13.29645 91.45118 0.15 0.885 -172.7624 199.3553 

SE08 | -183.3104 64.37775 -2.85 0.008 -314.2879 -52.33289 

_cons | 9196.633 1283.722 7.16 0.000 6584.882 11808.39 

Rejection of the null hypothesis supports the construct and suggests a stronger relation- 
ship between the 2008 Shadow Economy figures than the 1990 figures. Only 44.12% of the 
variation in the dependent variable is explained with the two SE variables. Regressing each sepa- 
rately as suggested by Gujarati, et al. (2009) shows the following results. 
Test Equation 3.4: Shadow Economy variables. 

Null Hypothesis: H : Ale = SE 1990 

Maintained Hypothesis: Hi: AIc^SEi 990 
Test: Linear Regression95% Confidence Level 
Regress dependent variable Ale with independent variable SE 1990 . 

Source | SS df MS Number of obs = 36 
+ F( lf 34) = 17.80 

Model | 114213558 1 114213558 Prob > F = 0.0002 

Residual I 218099590 34 6414693.81 R-squared = 0.3437 

+ Ad j R-squared = 0.3244 

Total | 332313147 35 9494661.35 Root MSE = 2532.7 

Dch3lc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

SE90 | -212.2003 50.28924 -4.22 0.000 -314.4003 -110.0002 
_cons | 8312.048 1369.584 6.07 0.000 5528.718 11095.38 

In this test, the null hypothesis is maintained. The SEi 990 explains about 32.4% of the 

variation in the Ale. The results are statistically significant at 95% level of certainty. 



200 



Test Equation 3.5: Shadow Economy variables. 

Null Hypothesis: H : Ale = SE 2 oo8 

Maintained Hypothesis: Hj : Ale ^ SE 2 oo8 
Test: Linear Regression95% Confidence Level 

Regress dependent variable Ale with independent variable SE 200 8- 

Source | SS df MS Number of obs = 36 
+ F( lf 34) = 30.49 

Model | 157117731 1 157117731 Prob > F = 0.0000 

Residual | 175195416 34 5152806.35 R-squared = 0.4728 

+ Ad j R-squared = 0.4573 

Total | 332313147 35 9494661.35 Root MSE = 2270 

Dch3lc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

SE08 | -175.2049 31.72893 -5.52 0.000 -239.6858 -110.7239 
_cons | 9243.678 1224.261 7.55 0.000 6755.681 11731.68 

The null hypothesis is rejected, and the temporary conclusion is that 45.73% of the varia- 
tion in the Ale can be attributed to the variations in the SE 20 o8- With a t-value of -5.52, an F-score 
of 30.49, and 35 degrees of freedom, this test passes the 2-t Rule of Thumb, and the negative sign 
is the anticipated correct sign. 



201 



Educational Expenditure data for the SET 

Test Equation 4: EE Data Validity; Comparing the means of the Set to the Mean of the Sample 

Set 

Recall that pre -test EE uses the average EE from 1980 to 1988, and the post-test EE uses 

the average EE from 1998 to 2007. An un-paired t-test compares the variances using a ratio. 
Null Hypothesis: H : Mean Ave EE of set ^ Mean Ave EE SET 
Maintained Hypothesis: Hj : Mean Ave EE of set = Mean Ave EE SET 

Test: Two-Sample Unpaired t-test99.9% Confidence Level 

Compare the Mean of the Ave EE Set to EE SET. 

Variable | Obs Mean Std. Err. Std. Dev. [99.9% Conf. Interval] 

+ 

AVE EE SET| 19 15.65927 1.678358 7.315792 9.077346 22.2412 
AVE EE Set 152 14.07912 .4289228 5.288115 12.6396 15.51863 

+ 

combined | 171 14.25469 .4240829 5.545604 12.83457 15.67481 

+ 

diff | 1.580154 1.347941 -2.934134 6.094442 

diff = mean (AVE EE SET) - mean (AVE EE Set) t = 1.1723 

Ho: diff = degrees of freedom = 169 

Ha: diff < Ha: diff != Ha: diff > 

Pr(T < t) = 0.8786 Pr ( | T | > |t|) = 0.2427 Pr (T > t) = 0.1214 

In this case, the null hypothesis is maintained. The report above maintain that there is a 
.87% probability that the true average of the EE for the SET et is less than the Total Set, and 
.1214% chance that it is greater than the average of the Total Set, yet still, it remains well within 
the acceptance area for the null hypothesis. The temporary conclusion is that the SET variance 
average represents the Total Set variance average for each at 99.9% confidence level (given that 
the Educational Expenditure of the Total Set is reliable (EdStats, 2010J; 17)). Further, that the 
sample set of data from the Central and Eastern European countries are valid, and that we can at- 
tempt to measure the degree of change in the Education Expenditures. 



202 



New Variables 
Equation: Ici + 1C2 = IC3. 

For example, the Official GDP 1990 in Ukraine was $243.35 billion in US equivalent dol- 
lars. The 16.3% SE 1990 was $39.66 billion, for a Total GDP 1990 of $283.01 billion. The equation 
for Ic 3 1990 is $4,716 Ici i 990 + $769 Ic 2 i 99 o = $5,485 in equivalent US dollars per capita. Stated 
otherwise, the official data say that the Ukrainian people earn $4,716 US equivalent dollars per 
person, but the SE makes up 16.3% of the total economy. Therefore, Ukrainians actually earn 
$5,485 US equivalent dollars. 

Equation: Example: EEj Ukraine + EE2 Ukraine — EE3 Ukraine 

15.95% !0f GE + - (16.3% SE x 15.95% GE) 2 = 13.7145 3 % of GE 
Alternate Equation, in US $Billions: 

15.95% of GE x $243.35 GDP! _ $3,881.43 GE 1 _ $3,881.43 GE 1 

$243.35 GDP!+ (16.3% x$243.35 GDPj) $243.35 GDP 1 +$39.661 GDP 2 ~~ $283.01 GDP 3 

= 13.7148% of GDP 3 Expenditures 

Otherwise stated, the official figure for EE is 15.95% of government spending. However, 
since the SE effectively keeps 16.3% of the potential government revenue out of the government 
budgets, the official number is overstated, and education actually gets to spend, all else equal, 
13.7148% of the GE budget. The SE is keeping for its use about $750 per Ukrainian citizen per 
year, given the policy is to invest 15.95% of its expenditure budget into public education. 
Regional Data Comparison for Sample Set Validity 
Research Question 1 

Research Question 1.1: Are the HDI and the change in the Income per capita correlated 
at .5 or higher? To test this construct with our data, we can run the correlation coefficient test. If 
we reject the null hypothesis, then we can conclude for now, that the correlation between the 
Human Development Index from 1990 to 2008 and the change in income per capita, adjusted for 
SE, (Ic 3 ), is less than .5, consistent with the rule used in Wong, (2007b). 

203 



Hypothesis 1.1: The correlation coefficient of Ale 3 from 1990 to 2008 and AUDI from 
1990 to 2008 is less than .5. 
Equation 1.1 

Null Hypothesis: H : if \t\ > t» n _ 2 : reject H 

2' 

Maintained Hypothesis: Hj : if \t\ < ta_ n _ 2 :fail to reject H 

2' 

The correlation coefficient is .2495, which is significantly less than the benchmark of .5. 
On a one -tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 de- 
grees of freedom at the 95% confidence level. For now, we maintain that the correlation between 
the change in the HDI is correlated with the change in Ic3, but at .2495, the degree of association 
is weak. Below, the scatter graph shows the weak correlation. 

To test the correlation between the Life Expectancy Index and the Educational Attain- 
ment Index, equally weighted (the weights in the HDI are equally weighted), we take the GDP 
index out, and re -run the correlation coefficient. 

Hypothesis 1.2: The correlation coefficient of Ale 3 from 1990 to 2008 and AHD1 com- 
ponent indices, ALE1 + EAlfrom 1990 to 2008 is less than .5. 
Equation 1.2 

Null Hypothesis: H : if \t\ > ta n _ 2 : reject H 

2' 

Maintained Hypothesis: Hi : if \t\ < ta n _ 2 :fail to reject H 

2' 

The correlation coefficient is .0015, which is significantly less than the benchmark of .5. 
On a one -tailed test, the t-statistic is -.501, well within the acceptance region of < .1697 at 30 de- 
grees of freedom at the 95% confidence level. For now, we maintain that the correlation between 
the change in the HDI is very slightly negatively correlated with the change in Ic3, at -.0015. Be- 
low is the scatter graph depicting the correlation between the Change in Income per Capita and 
the life expectancy and educational attainment indices. 

204 



Research Question 2 

Does governance corruption negatively affect Individual Income? (Governance corrup- 
tion is measured by the average Shadow Economy from 2000-2008, and Education expenditure is 
measured with the proxy EEc 3 , which includes the effect of the shadow economy on the total 
government expenditures). A linear regression comparison of the R 2 tests Research Question 2, 
using AIc 3 as the dependent and HDIi 990 as the independent variable. HDIi 990 is the pre-test or 
legacy measure, the starting point in human development measurements, for the Sample Set. 

Hypothesis 2: The adjusted R resulting from a linear regression of HDI against the AIc 3 
is higher than the adjusted R resulting from a linear regression of HDI and SE 2 oos against the 
AIc 3 . 
Equation 2. 1 

Null Hypothesis: Ho : AIc 3 ^ HDI1990 

Maintained Hypothesis: Hj : AIc 3 =HDI 1990 
Test: Linear Regression 95% Confidence Level 
Regressed dependent variable AIc 3 using independent variable HDIi 990 

Source | SS df MS Number of obs = 36 
+ F( x, 34) = 49.83 

Model I 197539878 1 197539878 Prob > F = 0.0000 

Residual I 134773269 34 3963919.67 R-squared = 0.5944 

+ Ad j R-squared = 0.5825 

Total | 332313147 35 9494661.35 Root MSE = 1991 

ch3tic | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

hdi90 | 36596.02 5184.047 7.06 0.000 26060.77 47131.27 
_cons | -26219.57 4126.185 -6.35 0.000 -34604.99 -17834.15 

Post-Estimation Statistics for Regression 

White's test for Ho: homoscedasticity 

against Ha: unrestricted heteroscedasticity 

chi2(2)= 12.49 
Prob > chi2= 0.0019 



205 



Cameron & Trivedi's decomposition of IM-test 



Source | 


chi2 


df 




p 


Heteroskedasticity | 


12.49 


2 





.0019 


Skewness | 


7.16 


1 


0. 


.0075 


Kurtosis | 


1.35 


1 


0. 


.2460 


+ 











Total | 21.00 4 0.0003 

Information Criteria 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -323.5223 2 651.0447 654.2117 

The regression output shows a high F-score at 49.83 with 35 degrees of freedom, at most, 
58.25% of the variation in the AIc 3 can be explained by the variation in the pre-test HDI, and the 
t- value of the HDI relationship is very significant at 7.06. This test passes the "2-f Rule of 
Thumb." The RMSE is 1991. "The minimum MSE criterion consists in choosing an estimator 
whose MSE is the least in a competing set of estimators. . .there is a trade-off involved - to obtain 
minimum variance, you may have to accept some bias" (Gujarati & Porter, 2009, p. 828). 
White's test confirms autocorrelation with X 2 of 12.49 on 2 degrees of freedom. The IM-test con- 
firms highly left skewed data at 7.16 and a slightly short and fat (platykurtic) kurtosis distribution 
at 1.35. The AIC is 651.0447. The analysis suggests rejecting the null hypothesis, confirming a 
significant relationship. The next test is a comparison of the R 2 values between this equation and 
a second equation adding SE 2 oo8 as an explanatory variable. 
Equation 2.2 

Null Hypothesis: 

H : R 2 regress AIc 3 with HDI 1990 > R 2 regress AIc 3 with HDI1990 and SE2008 

Maintained Hypothesis: 

H! : R 2 regress AIc 3 with HDI 1990 < R 2 regress AIc 3 with HDI 1990 SE 2 oo8 



206 



Test: Linear Regression95% Confidence Level 

Regressed dependent variable AIc 3 using independent variables HDI 1990 and SE 2 oo8- 

Source | SS df MS Number of obs = 36 
+ F( 2 , 33) = 33.80 

Model | 223306328 2 111653164 Prob > F = 0.0000 

Residual I 109006819 33 3303236.93 R-squared = 0.6720 

+ Ac jj R-squared = 0.6521 

Total | 332313147 35 9494661.35 Root MSE = 1817.5 

ch3tic | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

hdi90 | 26579.5 5937.794 4.48 0.000 14498.97 38660.03 

se08 | -89.02451 31.87514 -2.79 0.009 -153.875 -24.17404 

_cons | -15005.97 5505.278 -2.73 0.010 -26206.54 -3805.401 

Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 
against Ha: unrestricted heteroscedasticity 
chi2(5) = 12.09 

Prob > chi2= 0.0336 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 12.09 5 0.0336 
Skewness | 6.49 2 0.0389 
Kurtosis | 2.01 1 0.1560 
+ 

Total | 20.59 8 0.0083 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -319.703 3 645.406 650.1566 

The regression output shows a lower yet still very high F-score at 33.80 with 35 degrees 
of freedom, at most, 65.21% of the variation in the AIc 3 can be explained by the variation in the 
pre -test HDI, and both t- values HDL990 and SE 2 oos variables are high and significant at 4.48 and - 
2.79. This test passes the 2-t Rule of Thumb. The RMSE is lower at 1817.5. White's test rejects 
autocorrelation with X 2 of 12.09 on 5 degrees of freedom. The IM-test confirms a left skewed da- 
ta at 6.49 and less platykurtic at 2.41. The AIC is lower, at 645.406, which is preferred to the 
higher in Equation 2.1 of AIC 651.0447. Akaike's Information Criteria (AIC) states that when 

207 



"comparing two or more models, the model with the lowest value of AIC is preferred" (p. 494). 
The analysis suggests rejecting the null hypothesis, confirming a significant relationship on the 
second equation. 

A comparison of the R 2 test suggests rejecting the null hypothesis, and confirming for 
now that the R 2 of the augmented, second equation is higher, from 58.25% to 65.21%. In addi- 
tion, the entire equation is more robust with a lower RMSE, lower AIC, less skewness, and no 
autocorrelation. The F-score, which is lower yet still very high, explains that the shape of the dis- 
tribution is flatter. The rejected hypothesis suggests a temporary conclusion in favor of the SE 2 oo8 
per country included in the regression with the HDI 1990 , explains more of the variation in AIc 3 
than does the HDIi 99 o alone. This finding would be consistent with the theory that corruption 
hinders economic development, and of the findings of Schneider, et al. (2010), Kauffmann, et al. 
(2008), Johnston, (2007), and other scholars. 



208 



Research Question 3 

Does governance corruption negatively affect Education Expenditure? (Governance cor- 
ruption is measured by the average Shadow Economy from 2000-2008, and Education 
expenditure is measured with the proxy EE 3 ). A linear regression tests the effects of corruption 
on EEc 3 , by setting the change in EEc 3 (AEEc 3 ) as the dependent variable and the Shadow Econ- 
omy in 2008, SE 2( )08, as the dependent variable. 

Hypothesis 3: The variation in the AEEc 3 from 1990 to 2008 is not explained by the var- 
iation in SE2008- 
Equation 3 

Null Hypothesis: H : AEEc 3 ^ SE 2 oos 

Maintained Hypothesis: Hi : AEEc 3 = SE2008 
Test: Linear Regression 95% Confidence Level 
Regressed dependent variable $AEEc 3 using independent variable SE 2 oo8- 

Source | SS df MS Number of obs = 36 
+ F( lf 34) = 13.17 

Model I 2374199.26 1 2374199.26 Prob > F = 0.0009 

Residual I 6127530.8 34 180221.494 R-squared = 0.2793 

+ Ad j R-sq U ared = 0.2581 

Total | 8501730.06 35 242906.573 Root MSE = 424.53 

$AEEc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

se08 | -21.53734 5.933854 -3.63 0.001 -33.59639 -9.478302 
_cons | 1080.751 228.9578 4.72 0.000 615.4524 1546.049 

The test results suggest rejecting the null hypothesis, maintaining that the effects of the 
Shadow Economy on the change in Education Expenditures per person stated in dollars, SE 2 oos on 
$AEEc 3 are statistically significant. In addition, 25.81% of the variation in the change in Educa- 
tion Expenditures can be explained by variation in the Shadow Economy. The F-score is 13.17 
with 35 degrees of freedom and the t-value is -3.63 for SE 2 oos- The RMSE is 424.53. This test 
passes the "2-t Rule of Thumb." Figure 4.3 shows the effects of the Shadow Economy on Educa- 
tion Expenditures. Figure 4.4 shows the effects of the Shadow Economy on Income per Capita. 

209 



Research Question 4 

Do the pre -test HDI, governance corruption, and education expenditure explain the 
change in Income per capita? (Corruption is measured by the average Shadow Economy from 
2000-2008, and Education expenditure is measured with the proxy EEc 3 , which includes the ef- 
fect of the shadow economy on the total government expenditures). 

The null hypothesis asserts that there is no relationship between the $AIc 3 and the explan- 
atory variables, SE2008, and the percentage of change between the EEc 3 pretest and the posttest 
values, EEc3 1990 and EEc 3 2 oo8- Gujarati, et al. explains and supports the practice of adding varia- 
bles to seek higher degrees of significance and better over-all fit (2009, pp. 474-475). 

Hypothesis 4.1: The variation in the $AIc 3 from 1990 to 2008 is not explained by the var- 
iation in the HDI 1990 , the SE 2 oos and the EEc 3 in 19go and the EEc 3 in 2 oos- 
Equation 4. 1 

Null Hypothesis: H : AIc 3 ^ HDl 19yo + SE 20 os + EEc 3 1990 + EEc 3 2008 

Maintained Hypothesis: Hi : AIc 3 = HDI1990 + SE 2 oo8 + EEc 3 1990 + EEc 3 2 oos 

Test: Linear Regression 95% Confidence Level 

Regress dependent variable $AIc3 with independent variables HDI 1990, SE2008, EEc31990, 
EEc3 2008. 



Source | SS 

+ 

Model I 235595046 
Residual I 96718101 

+ 

Total I 332313147 



df 



MS 



4 58898761.6 
31 3119938.74 



35 9494661.35 



Number of obs = 


36 


F( 4, 31) = 


18.88 


Prob > F 


0.0000 


R-squared = 


0.7090 


Adj R-squared = 


0. 6714 


Root MSE 


1766.3 



$A3Ic I 



Coef. Std. Err. 



P> 1 1 1 



[95% Conf. Interval] 



11990 


30198.41 


6916.631 


4 


37 





000 


16091.84 


44304. 97 


SE08 


-69.00913 


32.80825 


-2 


10 





044 


-135.922 


-2.096266 


31990 


-105.0454 


79.86971 


-1 


32 





198 


-267. 9408 


57 .84992 


32008 


286.9724 


149.392 


1 


92 





064 


-17.71452 


591.6593 


cons 


-20081.12 


7128.202 


-2 


82 





008 


-34619.18 


-5543.054 



210 



Post-Estimation Statistics for Regression 

White's test for Ho: homoscedasticity 

against Ha: unrestricted heteroscedasticity 
chi2(14) =19.48 
Prob>chi2 =0.1475 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df 



+ 












Heteroskedasticity | 


19 


.48 


14 


0. 


.1475 


Skewness | 


5. 


.04 


4 


0. 


.2830 


Kurtosis | 


2. 


.41 


1 


0. 


.1205 


+ 













Total | 26.93 19 0.1063 

Ramsey RESET test using powers of the fitted values of A3Ic 

Ho: model has no omitted variables 
F(3, 14)=6.08 
Prob > F=0.0025 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -317.55 5 645.1001 653.0177 

The regression output shows an F-score of 18.88 with 35 degrees of freedom, at most, 
67.14% of the variation in the dollar change in total income per capita can be explained by the 
variation in the independent variables. The ?-values are all significant. This test does not pass the 
"2-t Rule of Thumb" as both of the education expenditure f-scores are less than 2.0 (Gujarati & 
Porter, 2009). The RMSE is 1766.3. The White's General Test for Heteroscedasticity reports a 
critical X 2 value of 19.48 which exceeds the X 2 score of 14, and which means heteroscedasticity 
exists (p. 387). The IM-test confirms highly left skewed data at 5.04, and a slightly platykurtic at 
2.41. The AIC is 645.1001. The analysis of the equation suggests rejecting the null hypothesis, 
confirming for now that a statistically significant relationship exists. 

The results of this test suggest that after accounting for corruption in the figures for each 
country, the change in income per person over the 18-year test period is a function of the devel- 
opment starting point in 1990 (HDI 19 9o), the average level of corruption from 2000 to 2008 

211 



(SE 2 oo8)> ar >d the percentage of the total expenditure budget set aside per person for public educa- 
tion in the pretest and posttest years (EEci 990 and EEc 2 oos)- However interesting these results, 
substituting the change in EEc 3 over the test period, may yield more significant results, as this 
method equalizes the pretest or starting point by country, and is consistent with the treatment of 
the Income variable (Gujarati & Porter, 2009). (See Table: 4.1 in the Appendix). 

Hypothesis 4.2: The variation in the $AIc3from 1990 to 2008 is not explained by the var- 
iation in the HDliggo, the SE2008 and the percent change in EEdfrom 1990 to 2008. 
Equation 4.2 

Null Hypothesis: H : $AIc 3 ^ HDI 1990 + SE 20 o8 + %AEEc 3 
Maintained Hypothesis: Hj : $AIc 3 = HDI 1990 + SE 2 oo8 + %AEEc 3 
Test: Linear Regression 95% Confidence Level 
Regressed dependent variable $AIc 3 using independent variables HDIi 990 , SE 2 oosand %AEEc 3 

Source | SS df MS Number of obs = 36 
+ F( 3/ 32) = 24.66 

Model I 231974312 3 77324770.5 Prob > F = 0.0000 

Residual I 100338836 32 3135588.62 R-squared = 0.6981 

+ Ad j R-squared = 0.6698 

Total | 332313147 35 9494661.35 Root MSE = 1770.8 

Dch3lc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 25815.11 5803.391 4.45 0.000 13993.99 37636.23 

SE08 | -79.11753 31.6222 -2.50 0.018 -143.5298 -14.70522 

CH3EEc | 889.5726 535.0346 1.66 0.106 -200.2573 1979.402 

_cons | -15091.7 5364.002 -2.81 0.008 -26017.81 -4165.58 

Post-Estimation Statistics for Regression 

White's test for Ho: homoscedasticity 

against Ha: unrestricted heteroscedasticity 
chi2(9)=14.27 
Prob >chi2=0. 8034 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 
+ 

Heteroskedasticity | 14.27 9 0.1130 
Skewness | 7.98 3 0.0464 
Kurtosis | 0.52 1 0.4702 

+ 

Total | 22.77 13 0.0445 

212 



Ramsey RESET test using powers of the fitted values of A3Ic 

Ho: model has no omitted variables 
F(3, 15)= 5.50 
Prob > F= 0.0041 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -318.2116 4 644.4232 650.7572 

The regression output shows an F-score of 24.66 with 35 degrees of freedom. At most, 
66.98% of the variation in the change in the dollars per capita, $AIc 3 , can be explained by the var- 
iation in the independent variables. The ?-values are all significant, however this test does not 
pass the "2-t Rule of Thumb" as the AEEc f-score is less than 2 at -1.66. The RMSE is 1770.8. 
The White's General Test for Heteroscedasticity reports a critical X 2 value of 14.27 which is 
greater than the X 2 score of 9, and which means heteroscedasticity is detected (Gujarati & Porter, 
2009, pp. 386-397). The IM-test confirms highly left skewed data at 7.98 and a platykurtic at .52. 
The AIC is 644. The analysis suggests rejecting the null hypothesis, confirming for now, a statis- 
tically significant relationship. 

As anticipated, 4.2 yielded a higher degree of "goodness of fit" (p. 386) between the var- 
iation in the change in income and the variation in the independent variables. Testing 4.2 using 
the change in the percent of spending on education per capita, however, will tend to provide a 
skew in the results that captures bigness in the change due to the medium-sized economies ability 
to adopt change, and not necessarily a better picture of the goodness of fit. This can be seen in 
Table 4, on the graphic comparison of these four equations. (See Table: 4.2 in the Appendix). 



213 



Hypothesis 4.: The variation in the $ Md from 1990 to 2008 is not explained by the var- 
iation in the HDIjggo, the SE2008 and the change in EE3froml990 to 2008. 
Equation 4.3 

Test: Linear Regression 95% Confidence Level 
Regressed dependent variable $AIc 3 using independent variables HDI 1990 , SE 2 oos and $AEE 3 

Source | SS df MS Number of obs = 36 
+ F( 2, 32) = 22.69 

Model I 226036995 3 75345665.1 Prob > F = 0.0000 

Residual I 106276152 32 3321129.75 R-squared = 0.6802 

+ Ad j R-squared = 0.6502 

Total I 332313147 35 9494661.35 Root MSE = 1822.4 



$A3lc I Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 I 25958.14 5993.159 4.33 0.000 13750.47 38165.8 
SE08 I -81.43262 33.0398 -2.46 0.019 -148.7325 -14.13275 
$A3EE I .017643 .0194572 0.91 0.371 -.02199 .0572759 
_cons I -14916.48 5521.05 -2.70 0.011 -26162.5 -3670.474 

Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 
against Ha: unrestricted heteroscedasticity 
chi2(9) =25.39 

Prob > chi2=0.0026 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 
+ 

Heteroskedasticity | 25.39 9 0.0026 
Skewness | 10.37 3 0.0157 
Kurtosis I 0.96 1 0.3283 

+ 

Total I 36.72 13 0.0005 

Ramsey RESET test using powers of the fitted values of A3Ic 
Ho: model has no omitted variables 
F(3, 15)=3.79 
Prob > F=0.0207 

Akaike's Information Criteria Score of the Model 

Model I Obsll (null) 11 (model) dfAICBIC 

. |36 -339.767 -319.24644 646.4927 652.8268 



214 



The regression output shows a high F-score at 22.69 with 35 degrees of freedom, at most, 
65.02% of the variation in the $AIc 3 can be explained by the variation in the independent varia- 
bles, and the t- values are all significant. This test does not pass the "2-t Rule of Thumb" as the 
change in the total education expenditures dollars per country, $AEE 3 , f-score is less than 2 at .91 
(Gujarati & Porter, 2009). The RMSE is 1822.4. The White's General Test for 
Heteroscedasticity reports a critical X 2 value of 25.39 which greater than the X 2 score of 9, and 
which means heteroscedasticity exists (pp. 386-397). The IM-test confirms left skewed data at 
10.37 and a platykurtic at .96. The AIC is 646.4927. The analysis of the equation suggests re- 
jecting the null hypothesis and confirming for now that a statistically significant relationship 
exists. 

Testing this equation using the change in the dollars spent, however, will tend to provide 
a skew in the results that captures bigness in the available budget due to the larger economy, and 
not necessarily a better picture of the goodness of fit. This can be seen in Table 4, on the graphic 
comparison of these four equations. (See Table: 4.3 in the Appendix). 

Hypothesis 4.4: The variation in the $AIc 3 from 1990 to 2008 is not explained by the var- 
iation in the HDI iggo , the SE 2 oos and the dollar change in EEc 3 per capita froml 990 to 2008. 
Equation 4.4 

Null Hypothesis: H : $AIc 3 ^ HDI 1990 + SE 20 o8 + $AEEc 3 

Maintained Hypothesis: Hi : $AIc 3 = HDI1990 + SE2008 +$AEEc 3 



215 



Test: Linear Regression 95% Confidence Level 

Regressed dependent variable AIc 3 using independent variables HDI 1990 , SE 2 oo8 and $AEEc3 

Source | SS df MS Number of obs = 36 
+ F( 3/ 32) = 81.38 

Model | 293803978 3 97934659.2 Prob > F = 0.0000 

Residual | 38509169.7 32 1203411.55 R-squared = 0.8841 

+ Ad j R-squared = 0.8733 

Total | 332313147 35 9494661.35 Root MSE = 1097 

Dch3lc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 16384.67 3823.468 4.29 0.000 8596.518 24172.82 

SE08 | -44.14497 20.11301 -2.19 0.036 -85.11383 -3.176101 

DCH3EEc | 3.618593 .4727804 7.65 0.000 2.655571 4.581615 

_cons | -9615.573 3396.708 -2.83 0.008 -16534.44 -2696.706 

Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 
against Ha: unrestricted heteroscedasticity 
chi2(9)=8.73 
Prob > chi2 =0.4624 

Cameron & Trivedi's decomposition of IM-test 

Source | chi2 df p 

+ 

Heteroskedasticity | 8.73 9 0.4624 
Skewness | 6.33 3 0.0964 
Kurtosis | 0.06 1 0.8014 

+ 

Total | 15.13 13 0.2993 

Ramsey RESET test using powers of the fitted values of A3Ic 
Ho: model has no omitted variables 
F(3, 16)=1.38 
Prob > F=0.2695 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -300.9738 4 609.9475 616.2816 

The regression output shows the highest measure of goodness of fit of the four equations 
with an F-score of 81.38 and 35 degrees of freedom, at most, 87.33% of the variation in the $AIc3 
can be explained by the variation in the independent variables, and the f-values are all significant. 
This test passes the "2-t Rule of Thumb" (Gujarati & Porter 2009). The RMSE is 1097, the low- 



216 



est of the four equations. The White's General Test for Heteroscedasticity reports a critical X 2 
value of 8.73 which is smaller than the X 2 score of 9, and which means heteroscedasticity does 
not exist (pp. 386-397). The IM-test confirms left skewed data at 6.33 and a platykurtic at .06. 
The AIC is 609.9475. The analysis of the equation suggests rejecting the null hypothesis and 
confirming for now that a statistically significant relationship exists. (See Table: 4.4 in the Ap- 
pendix). 

Testing this equation using the change in the dollars spent per person, will tend to provide 
a skew in the results that underestimates spending in larger economies, and overestimates smaller 
economies possibly missing a variable such as efficiency or effectiveness in the education system. 
Equation 4.4 is the highest scoring equation in each of the categories. This can be seen on Table: 
4.5 in the Appendix, which is the graphic comparison of these four equations. 

Hypothesis 4.5: The variation in the $ Ale 3 from 1990 to 2008 is not explained by the 
variation in the HDI1990, the SE2008, the dollar change in EEc3 per capita froml 990 to 2008 
and the Country Group. 
Equation 4.5 

Null Hypothesis: H : $AIc 3 ^ HDli 9yo + SE 2 oo8 + $AEEc 3 + Group 

Maintained Hypothesis: IT : $AIc 3 = HDI 19 9 + SE 2 oo8 +$AEEc 3 + Group 
Test: Linear Regression 95% Confidence Level 
Regressed variable AIc 3 using independent variables HDIi 990 , SE 2 oo8, $AEEc3, and Group 

Source | SS df MS Number of obs = 36 
+ F( 4/ 31) = 59.30 

Model | 293904763 4 73476190.8 Prob > F = 0.0000 

Residual I 38408384 31 1238980.13 R-squared = 0.8844 

+ Ad j R-squared = 0.8695 

Total | 332313147 35 9494661.35 Root MSE = 1113.1 



$Alc3 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI 1990 I 16208.84 3928.236 4.13 0.000 8197.152 24220.53 

SE 2008 | -40.91068 23.34705 -1.75 0.090 -88.5273 6.705943 

$AEEc3 | 3.581477 .4970541 7.21 0.000 2.567729 4.595226 

Group | 109.3131 383.2702 0.29 0.777 -672.3716 890.9979 

_cons | -9775.285 3491.734 -2.80 0.009 -16896.72 -2653.846 

217 



Post-Estimation Statistics for Regression 

White's test for Ho:homoscedasticity 
against Ha: unrestricted heteroscedasticity 
chi2(14) = 13.01 
Prob > chi2= 0.5256 

Cameron & Trivedi's decomposition of IM-test 



Source | 


cl 


ii2 


df 




P 


Heteroskedasticity | 


13. 


.01 


14 


0. 


.5256 


Skewness | 


5. 


.35 


4 





.2535 


Kurtosis | 





.06 


1 


0. 


.7995 


Total | 


18. 


.42 


19 


0. 


.4944 



Ramsey RESET test using powers of the fitted values of $ AIc3 
Ho: model has no omitted variables 
F(3,28)= 1.42 
Prob > F = 0.2564 

Akaike's Information Criteria Score of the Model 

Model | Obs 11 (null) 11 (model) df AIC BIC 

+ 

. | 36 -339.767 -300.9266 5 611.8532 619.7708 

The regression output shows the second highest measure of goodness of fit of the four 
equations with an F-score of 59.3 and 35 degrees of freedom, at most, 86.99% of the variation in 
the $AIc3 can be explained by the variation in the independent variables, and the ?-values are all 
significant. This test does not pass the "2-f Rule of Thumb" for the Group or the Shadow Econ- 
omy variables (Gujarati & Porter 2009). The RMSE is 1 1 13.1. The White's General Test for 
Heteroscedasticity reports a critical X 2 value of 13.01 which is smaller than the X 2 score of 14, 
and which means heteroscedasticity does not exist (pp. 386-397). The IM-test confirms left 
skewed data at 5.35 and a platykurtic at .06. The AIC is 61 1.85. The analysis of the equation 
suggests rejecting the null hypothesis and confirming for now that a statistically significant rela- 
tionship exists. 

Adding the Country Group to the equation tended to absorb the skewness in the results 
and the significance of the Shadow Economy, possibly suggesting that the Country Group may 

2tt 



approximate the degree of the Shadow Economy of a country in this sample set. Table: 4.5 in the 
Appendix, is the graphic comparison of these equations. Table 5.1 in the Appendix provides the 
Correlation Coefficient for the variables. The Country Group degree of association with the Total 
Change in Income per Capita is 0.6549, with the HDI in 1990 is 0.5661, with the Education Ex- 
penditure is 0.5648, and with the Shadow Economy in 1990 is -0.6958. 

The widely accepted method for determining the factors of economic growth is the OLS 
linear regression, (e.g., Sachs and Werner (1995) Economic Convergence and Economic Policies, 
Gupta et al., (1998) Does Corruption Affect Income Inequality and Poverty?). However, the 
structural equation method appears in important articles since 1984, where Frey and Weck- 
Hannemann (1984) apply the MIMIC method used in psychometrics starting in the 1970s in The 
Hidden Economy as an "Unobserved" Variable, and Sachs and Werner (1997) apply the Two- 
Stage Least Squared method in Fundamental Sources of Long-Run Growth. 

The relatively new body of literature delineating corruption from the underground - or 
shadow, parallel, off the books, non-observed - economy makes use of the complex and relative- 
ly new structural equation approach or MIMIC model, which stands for Multiple Indicator 
Multiple Cause. "The idea is to represent the output (or income) of the underground economy as 
a latent variable or index, which has causes and effects that are observable but which cannot itself 
be directly measured" (Breuch, 2005, p. 1). 

Thus, there are two kinds of 2 observed variables in the model, "causal" variables and 
"indicator" variables, which are connected by a single unobserved index. Values of the index 
over time are inferred from data on causes and indicators by estimating the statistical model and 
predicting the index. The fitted index is then interpreted as a time-series estimate of the magni- 
tude of the underground economy. Usually the measure is hidden output or income as a 
percentage of recorded GDP, although some researchers are concerned with the "tax gap" be- 
tween actual revenue and the potential revenue when all taxable income is reported (p. 1). 

219 



Critics of the MIMIC method cite instability in the findings with minor changes in the pe- 
riod or the countries studied, the absence of important economic, political, or social influences in 
the embodied in the variables, and the reliance on multiple and different variables for each equa- 
tion (p. 2). While employing a simultaneous equation method would allow us to solve for the 
effects of the shadow economy on both income and education in isolation and together and poten- 
tially report preliminary information on causality, this thesis' relatively simple data set tested 
using the complex MIMIC method is be beyond the scope of this thesis and possibly of the avail- 
able data. 

The Two Stage Least Squared method (2SLS), however, is within the possibilities of 
methods useful to this thesis and potentially helpful given that the variables are endogenous, and 
likely correlated with the error term, violating the rules of OLS regression assumptions with bias 
in the test results (Nagler, 1999). From Leigh and Schimbri (2004, p. 286-7) comes the logic of- 
fered in the following discussion regarding the 2SLS method. To estimate the causal effect of the 
Shadow Economy on Income per Capita, we can use an instrument, which affects Income only 
through its effect on the Shadow Economy. Correlation between Income and the Shadow Econ- 
omy does not imply that the Shadow Economy causes lower Income because other variables, 
such as regime changes or armed conflicts, may affect both Income and the Shadow Economy. In 
addition, Income may affect the Shadow Economy in addition to the Shadow Economy causing 
changes in Income, in a cyclical relationship. To attempt to estimate the causal effect of the 
Shadow Economy on Income from the sample data, we use the Education Expenditures as an in- 
strument for the Shadow Economy in an Income regression. If Education Expenditures only 
affect Income per Capita because it affects the Shadow Economy (ceteris paribus), correlation be- 
tween Education Expenditures and Income is evidence that Shadow Economy causes changes in 
Income. An estimate of the effect of the Shadow Economy on Income can be made by also mak- 
ing use of the correlation between Education Expenditures and the Shadow Economy patterns. 

220 



Research Question 4.6 

The null hypothesis asserts that there is no significant relationship between the level of 
Individual Income 3 in 2008 (Ic 3 08) and the explanatory variables, HDI1990, SE08, and the level 
of Education Expenditures per Capita in 2008 (EE 3 08Dc) 

Hypothesis 4.6: The variation in the IC3O8 is not explained by the variation in the 
HDI1990, the SE08, and the level ofEEc 3 08Dc. 
Equation 4.6 

Null Hypothesis: H : Ic 3 08 ± HDD 990 + SE08 + EE 3 08Dc 
Maintained Hypothesis: Hi : Ic308 = HDI1990 + SE08 + EE308Dc 
Following is the ST ATA output for the single equation instrumental variables 2SLS re- 
gression equation reporting small sample results that are adjusted for the degrees of freedom. In 
the first stage, Education Expenditures per Capita in 2008 are the dependent variable. The Shad- 
ow Economy in 2008 and the Human Development Index in 1990 are the independent variables. 
The second stage sets Individual Income in 2008 as the dependent variable, and the employs the 
independent variables in the first stage as instrumental variables and tests their joint effect along 
with Education Expenditures on Individual Income. 
Test: ivregress 2sls Ic32008 SE08 (EE308Dc = HDI1990), first small 



First-stage regressions 


Number of ob 


s 36 










F( 2, 33) = 


26.77 








Prob > F 


0.0000 








R-squared = 


0.6187 








Adj R-squared = 


0.5956 








Root MSE 


667.8208 


EE308DC 1 


Coef . 


Std. Err. t 


P>|t| [95% Conf. 


Interval] 


SE08 | 


-26.1214 


11.70901 -2.23 


0.033 -49.94357 


-2.299236 


HDI1990 | 


9073.607 


2155.828 4.21 


0.000 4687.542 


13459.67 


cons | 


-5219.72 


1999.873 -2.61 


0.014 -9288.493 


-1150.948 



221 



Instrumental variables (2SLS) regression (with adjustments to the degrees of freedom on a small 
sample) 

Source | SS df MS Number of obs = 36 
+ F( 2 , 33) = 162.38 

Model | 2.8105e+09 2 1.4052e+09 Prob > F = 0.0000 

Residual I 215380231 33 6526673.65 R-squared = 0.9288 

+ Ad j R-squared = 0.9245 

Total | 3.0258e+09 35 86452635.7 Root MSE = 2554.7 

Ic32008 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

EE308Dc | 9.329588 .9089084 10.26 0.000 7.4804 11.17878 

SE08 | -7.666987 62.0813 -0.12 0.902 -133.9723 118.6384 

_cons | -743.211 3103.729 -0.24 0.812 -7057.795 5571.373 

Instrumented: EE308Dc, Instruments: SE08 HDI1990 

Instrumental variables (2SLS) regression (without adjustments) 

Number of obs = 36 

Wald chi2 (2) = 354.29 

Prob > chi2 = 0.0000 

R-squared = 0.9288 

Root MSE = 2446 

Ic32008 | Coef. Std. Err. z P> | z | [95% Conf. Interval] 

+ 

EE308Dc | 9.329588 .8702136 10.72 0.000 7.624001 11.03518 

SE08 | -7.666987 59.43832 -0.13 0.897 -124.1639 108.83 

_cons | -743.211 2971.594 -0.25 0.803 -6567.428 5081.006 

The regression output shows a high F-score at 26.77 with 35 degrees of freedom, at most, 
59.56% of the variation in the Education Expenditures per capita in 2008 is explainable by the 
variation in the Shadow Economy on average from 2000-2007 and the Human Development In- 
dex 1990, at the 95% confidence level. The signs are correct. This test does not pass the "2-t 
Rule of Thumb" as the combined t-score of the Shadow Economy 08 and the HDI 1990 on Edu- 
cation Expenditures per Capita in 2008 is less than 2, at -0.12 (Gujarati & Porter 2009). The test 
results suggest that Individual Income in 2008 is a function of Education Expenditures, both of 
which are affected by the Shadow Economy, and both the Income and Education variables are af- 
fected by the initial level of Human Development. This methodological option suggests a causal 
relationship from the HDI 1990 level and Shadow Economy percentage toward the Education 
Expenditures and Income variables. The analysis of the equation suggests rejecting the null hy- 

222 



pothesis and confirming for now that a statistically significant and causal relationship exists be- 
tween the independent variables, HDI1990, the Shadow Economy in 2008, and Education Ex- 
Expenditures in 2008; on Individual Income in 2008. 

However, the high Wald-Wolfowitz score of 354.29, along with the Hausman test of 
Endogeneity that shows a significant coefficient of the EE308Dc in the second stage with a high 
t-score of 10.72 suggests that the new variables are not independent (p. 705). 

In Notes on Simultaneous Equations and Two Stage Least Squares Estimates, Nagler 
(1999, p. 7) cautions the researcher about shortcomings of the 2SLS estimates; consistent results 
require large samples, the B] will be consistent but asymptotic to infinity toward zero, and there- 
fore, will not be unbiased. If one considers the sample set to be relatively small, n=36, and 
considers that the 2SLS method may report inconsistent B ; results which may be (diminishing 
marginally) biased, it may be less attractive a method option than is OLS as the simplest method 
is desirable. However, we decide against the simultaneous equation option at the expense of the 
preliminary information on causality (Gujarati & Porter 2007, p. 96). Further analysis may yield 
a more telling decision rule. 

The most common method found in the literature on the effects of corruption and the de- 
terminants of economic growth is, by far, the Ordinary Least Squared (OLS) linear regression. 
An important example of its use is found in Gupta, Davoodi, and Alonso-Term (1998). Does 
Corruption Affect Income Inequality and Poverty? International Monetary Fund Working Paper, 
98(76), 1-41. The benefits of a linear regression include that we learn more about the relation- 
ships among and between several independent variables and a dependent variable in a simple 
model. In addition, we can 'control a variable' if we wish to balance the effect of that variable 
across variables - - so that we can minimize differences statistically - - and just study the relation- 
ship between the independent and the dependent variables. The limitations of regression 
techniques include three issues found here. (1) We can only deduce relationships and cannot be 

223 



sure about underlying causal mechanism. (2) Inherent in these techniques is the tendency toward 
closely to fully redundant variables, collinearity. (3) Violating the assumptions of normal distri- 
butions in the variance (and then in the standard errors), heteroscedasticity, may lead to bias in 
the inferences made from the resulting tests (Gujarati & Porter 2009). 

Several tests that offer decision rules exist. Using the sample size decision rule, we 
would "reject the null hypothesis when the computed F-value exceeds the logarithm of the sam- 
ple size (n=36 = In 1.55), which it does in both the 2SLS and OLS equations, so neither method is 
favored based on the sample size decision rule. If one assumes that n=36 is relatively large sam- 
ple size and employs the 2SLS equation, the decision rule falls to the tests for endogeneity. If the 
null hypothesis states that the variables are not endogenous, we would reject the null hypothesis 
in favor of the maintained hypothesis, that the variables are likely not independent, and opt for the 
OLS equation. According to Gujarati & Porter (p 828), "[i]n practice, the minimum MSE criteri- 
on is used when the best unbiased estimators are incapable of producing estimators with smaller 
variances" as seen in these examples. One would favor the OLS test results of a 2146.2 MSE 
over the 2SLS test results of a 2446 MSE, and accept the tradeoff of a smaller MSE, at the ex- 
pense of some bias. Rather disregard the learning from 2SLS method, a researcher may employ 
other tests for causality; however, these may be best reserved for future study. For the purposes 
of this thesis, the OLS method is maintained as the best linear unbiased estimator (p. 422). 
Test: Regress Ic32008 HDI1990 SE08 EE308Dc 

Source | SS df MS Number of obs = 36 
+ F( 3/ 32) = 208.31 

Model | 2.8784e+09 3 959482690 Prob > F = 0.0000 

Residual I 147394179 32 4606068.1 R-squared = 0.9513 

+ Ad j R-squared = 0.9467 

Total | 3.0258e+09 35 86452635.7 Root MSE = 2146.2 



Ic32008 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 19501.72 8588.73 2.27 0.030 2007.052 36996.39 

SE08 | -63.8092 40.36713 -1.58 0.124 -146.0344 18.41596 
EE308Dc | 7.180308 .5594332 12.83 0.000 6.04078 8.319836 

cons | -11961.85 7059.257 -1.69 0.100 -26341.09 2417.384 



224 



From Equation 4.4, the 2SLS method yields results consistent with those in Equation 4.6, 
and the same decision is made as was made in Equation 4.6; the OLS method remains the best 
linear unbiased estimator of Equation 4.4 using the minimum MSE decision rule. 

The STATA command reads: Instrumental Variable 2SLS, where the first the Change in 
Total Education Expenditures per Capita from 1990 to 2008 stated in dollars is regressed against 
the average Shadow Economy from 2000 to 2008 and the Human Development Index in 1990. 
Test: Inverse Regress 2sls Ic32008 HDI1990 (EE3ChDc = SE08), first 
First-stage regressions 



Number 


of obs = 




36 








F( 2, 


33) 


= 




9.80 




Prob > F 




= 


0. 


.0005 




R-squared 




= 


0. 


.3727 




Adj R-squared 


= 


0. 


.3347 




Root MSE 




= 


402 


.0028 



EE3ChDc | Coef. 


Std. Err. 




t 


P>|t| 


[95% Conf. 


Interval] 


HDI1990 | 2877.536 


1297.727 


2 


.22 


0.034 


237.2912 


5517.78 


SE08 | -12.10331 


7.048382 


-1 


.72 


0.095 


-26.44335 


2.236729 


_cons | -1544.868 


1203.848 


-1 


.28 


0.208 


-3994.114 


904.3786 


Instrumental variables (2SLS) regression 








Number of 


obs = 3 6 
Wald chi2 (2) 
Prob > chi2 
R-squared 
Root MSE 


68.08 

= 0.0000 

= 0.6296 

5580 


Ic32008 | Coef. 


Std. Err. 




z 


P>|z| 


[95% Conf. 


Interval] 



EE3ChDc | 20.76861 8.083341 2.57 0.010 4.92555 36.61167 

HDI1990 | 24890.61 37032.29 0.67 0.501 -47691.35 97472.57 

cons | -17356.3 27202.64 -0.64 0.523 -70672.48 35959.89 



Instrumented: EE3ChDc Instruments: HDI1990 SE08 

The STATA command that adjusts for the small sample size does not change the decision rule. 



225 



Test: ivregress 2sls Ic32008 HDI1990 (EE3ChDc = SE08), first small 



First-stage regressions 



Number of obs = 


36 


F( 2, 33) = 


9.80 


Prob > F 


0.0005 


R-squared = 


0.3727 


Adj R-squared = 


0.3347 


Root MSE 


402.0028 



EE3ChDc | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 2877.536 1297.727 2.22 0.034 237.2912 5517.78 

SE08 | -12.10331 7.048382 -1.72 0.095 -26.44335 2.236729 

_cons | -1544.868 1203.848 -1.28 0.208 -3994.114 904.3786 

Instrumental variables (2SLS) regression 

Source | SS df MS Number of obs = 36 
+ F( 2 , 33) = 31.20 

Model | 1.9049e+09 2 952464510 Prob > F = 0.0000 

Residual | 1.1209e+09 33 33967067.5 R-squared = 0.6296 

+ Ad j R-squared = 0.6071 

Total | 3.0258e+09 35 86452635.7 Root MSE = 5828.1 

Ic32008 I Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

EE3ChDc | 20.76861 8.442775 2.46 0.019 3.591654 37.94556 

HDI1990 | 24890.61 38678.97 0.64 0.524 -53802.35 103583.6 

_cons | -17356.3 28412.23 -0.61 0.545 -75161.4 40448.81 

Instrumented: EE3ChDc Instruments: HDI1990SE08 
Regress Ic32008 HDI1990 SE08 EE3ChDc 

Source | SS df MS Number of obs = 36 
+ F( 2, 32) = 63.79 

Model | 2.5924e+09 3 864128087 Prob > F = 0.0000 

Residual | 433457989 32 13545562.2 R-squared = 0.8567 

+ Ad j R-squared = 0.8433 

Total | 3.0258e+09 35 86452635.7 Root MSE = 3680.4 

Ic32008 | Coef. Std. Err. t P> I t | [95% Conf. Interval] 

+ 

HDI1990 | 57561.19 12735.36 4.52 0.000 31620.12 83502.26 

SE08 | -137.417 67.35091 -2.04 0.050 -274.6063 -.2276653 

EE3ChDc | 9.41494 1.593722 5.91 0.000 6.168635 12.66125 

cons | -34896.21 11293.16 -3.09 0.004 -57899.63 -11892.79 



226 



TABLES AND FIGURES 



227 



Table 1 Hypothesis 1 Data. 





o 

ON 
ON 

X 

u 

-a 

£ 
a 
1 

Oh 

> 

O 

Q 
a 

6 

3 

X 


Life Expectancy Index plus 
Educational Attainment Index 


Change in the Human Developme 
Index 1990 to 2008 


oo 

o 

o 

<N 

c 
ca 

w 

'Eh 

u 

u 

Oh 
U 

a 

o 
o 

a 


$ Change in Income per Capita 
from 1990 to 2008 


Country 


HDI1990 


HDI2 


HDICh 


Ic 2008 


IcCh $c 


Armenia 


0.73 


111 


150.70 


1,520 


725 


Azerbaijan 


0.76 


109 


142.91 


2,131 


880 


Belarus 


0.80 


136 


169.59 


2,515 


1,105 


Estonia 


0.82 


153 


186.75 


7,114 


3,291 


Georgia 


0.74 


114 


152.60 


1,271 


-222 


Kazakhstan 


0.78 


110 


140.04 


2,380 


768 


Kyrgyzstan 


0.69 


109 


158.05 


379 


-86 


Latvia 


0.80 


153 


189.60 


6,036 


2,135 


Lithuania 


0.83 


155 


185.97 


6,032 


1,741 


Moldova 


0.74 


124 


167.10 


581 


-248 


Russian Federation 


0.82 


147 


177.89 


3,043 


441 


Tajikistan 


0.64 


106 


165.50 


245 


-181 


Turkmenistan 


0.73 


106 


143.87 


1,714 


672 


Ukraine 


0.75 


118 


155.10 


1,156 


-231 


Uzbekistan 


0.69 


110 


158.86 


840 


155 


Albania 


0.78 


104 


131.52 


1,825 


847 


Bosnia Herzegovina 


0.80 


119 


146.87 


2,223 


-1,194 


Bulgaria 


0.80 


131 


162.41 


2,661 


991 


Croatia 


0.82 


124 


150.71 


6,807 


1,552 


Czech Republic 


0.85 


158 


185.65 


7,593 


2,257 


Germany 


0.90 


184 


203.80 


25,547 


6,119 


Hungary 


0.81 


160 


196.54 


6,216 


1,976 


Macedonia 


0.78 


123 


156.75 


2,175 


115 


Mongolia 


0.68 


121 


177.67 


735 


239 


Montenegro 


0.82 


128 


155.52 


2,335 


890 


Poland 


0.81 


146 


180.30 


6,228 


3,132 


Romania 


0.79 


134 


170.00 


2,845 


949 


Serbia^ 


0.80 


124 


154.03 


328 


-1,117 


Slovakia 


0.83 


162 


194.35 


8,591 


3,381 


Slovenia 


0.85 


125 


145.63 


13,789 


5,472 


Average 


0.78 


130.02 


165.21 


4228.50 


1218.49 


Standard Deviation 


0.06 


20.85 


18.87 


5100.83 


1702.22 


Minimum 


0.06 


20.85 


18.87 


244.96 


-1194.27 


Maximum 


0.90 


183.50 


203.80 


25546.85 


6119.18 



228 



Table 2 Hypothesis 2 Data. 



1 

03 


o 

ON 
ON 

X 
U 

•a 
£ 
a 

g 

Oh 
O 

13 
> 
u 

Q 

e 

03 

a 

3 


$ Change in Income per Capita 
from 1990 to 2008 


Shadow Economy Ave as a 
percentage of GDP 2000-07 


Country 


HDI1990 


IcCh $c 


SE2008 


Armenia 


0.731 


724.55 


48.70 


Azerbaijan 


0.755 


880.49 


63.30 


Belarus 


0.795 


1105.22 


49.80 


Estonia 


0.817 


3291.29 


40.30 


Georgia 


0.739 


-221.71 


68.80 


Kazakhstan 


0.778 


768.37 


45.30 


Kyrgyzstan 


0.687 


-85.87 


42.00 


Latvia 


0.803 


2134.96 


41.70 


Lithuania 


0.828 


1741.07 


31.90 


Moldova 


0.735 


-248.10 


45.80 


Russian Federation 


0.821 


441.25 


48.60 


Tajikistan 


0.636 


-181.11 


44.30 


Turkmenistan 


0.730 


672.41 


36.00 


Ukraine 


0.754 


-230.75 


53.90 


Uzbekistan 


0.687 


154.85 


37.93 


Albania 


0.784 


846.86 


36.30 


Bosnia Herzegovina 


0.803 


-1194.27 


34.60 


Bulgaria 


0.803 


990.74 


37.50 


Croatia 


0.817 


1551.59 


34.70 


Czech Republic 


0.847 


2257.16 


19.80 


Germany 


0.896 


6119.18 


16.10 


Hungary 


0.812 


1975.51 


25.80 


Macedonia 


0.782 


115.24 


36.20 


Mongolia 


0.676 


239.23 


37.90 


Montenegro 


0.815 


889.86 


39.67 


Poland 


0.806 


3131.82 


28.00 


Romania 


0.786 


949.11 


36.30 


Serbia 


0.797 


-1116.71 


39.67 


Slovakia 


0.827 


3380.84 


19.70 


Slovenia 


0.853 


5471.62 


28.00 


Average 


0.780 


1218.49 


38.95 


Standard Deviation 


0.058 


1702.22 


11.66 


Minimum 


0.058 


-1194.27 


11.66 


Maximum 


0.896 


6119.18 


68.80 



229 



Table 3 Hypothesis 3 Data. 



1 


Shadow Economy Ave as a 
percentage of GDP 1989-99 


Shadow Economy Ave as a 
percentage of GDP 2000-07 


Dollars Spent on education per 
capita 


Country 


SE 1990 


SE2008 


EEDc 

2008 


Armenia 


40.300 


48.70 


252.07 


Azerbaijan 


45.100 


63.30 


227.73 


Belarus 


35.600 


49.80 


304.95 


Estonia 


34.300 


40.30 


1,094.55 


Georgia 


45.100 


68.80 


166.74 


Kazakhstan 


31.900 


45.30 


252.23 


Kyrgyzstan 


35.200 


42.00 


96.16 


Latvia 


25.700 


41.70 


1,554.71 


Lithuania 


26.000 


31.90 


983.42 


Moldova 


29.300 


45.80 


179.31 


Russian Federation 


27.800 


48.60 


320.32 


Tajikistan 


24.000 


44.30 


65.03 


Turkmenistan 


24.000 


36.00 


304.27 


Ukraine 


29.400 


53.90 


220.54 


Uzbekistan 


22.100 


37.93 


176.27 


Albania 


31.000 


36.30 


194.86 


Bosnia Herzegovina 


28.000 


34.60 


474.28 


Bulgaria 


27.100 


37.50 


297.04 


Croatia 


24.600 


34.70 


722.31 


Czech Republic 


13.100 


19.80 


707.96 


Germany 


12.200 


16.10 


2,397.18 


Hungary 


22.300 


25.80 


711.57 


Macedonia 


35.600 


36.20 


412.96 


Mongolia 


18.100 


37.90 


132.62 


Montenegro 


23.600 


39.67 


338.04 


Poland 


22.300 


28.00 


785.04 


Romania 


27.300 


36.30 


370.49 


Serbia 


23.600 


39.67 


46.02 


Slovakia 


15.100 


19.70 


971.98 


Slovenia 


22.900 


28.00 


1,791.24 


Average 


27.420 


38.95 


551.73 


Standard Deviation 


8.128 


11.66 


554.25 


Minimum 


8.128 


11.66 


46.02 


Maximum 


45.100 


68.80 


2,397.18 



230 



Table 4 Hypothesis 4 Data. 



1 


o 

ON 
ON 

X 

•a 

a 

S3 

1 

& 
JO 

> 

Q 

a 

s 

E 


Dollar Difference in Education 
Expenditures per Capita 


Dollar Change Shadow Economy 
Dollar Value per Capita 


Dollar Change in Official Income 
per Capita from 1990 to 2008 


Dollar Change in Total Income pe 
Capita from 1990 to 2008 


Country 


HDI1990 


EED 

Ch/c 


SEDCh 


IcCh $c 


IcT 
ChDc 


Armenia 


0.731 


106.755 


419.67 


725 


1144.21 


Azerbaijan 


0.755 


-105.747 


784.98 


880 


1665.47 


Belarus 


0.795 


63.874 


750.59 


1105 


1855.80 


Estonia 


0.817 


119.845 


1555.73 


3291 


4847.02 


Georgia 


0.739 


58.304 


201.29 


-222 


-20.41 


Kazakhstan 


0.778 


-52.136 


564.04 


768 


1332.41 


Kyrgyzstan 


0.687 


-11.236 


-4.45 


-86 


-90.32 


Latvia 


0.803 


899.312 


1514.47 


2135 


3649.43 


Lithuania 


0.828 


102.591 


808.55 


1741 


2549.62 


Moldova 


0.735 


-45.103 


23.17 


-248 


-224.93 


Russian Federation 


0.821 


70.509 


755.71 


441 


1196.96 


Tajikistan 


0.636 


-59.929 


6.26 


-181 


-174.85 


Turkmenistan 


0.730 


49.037 


367.07 


672 


1039.49 


Ukraine 


0.754 


-117.112 


215.34 


-231 


-15.41 


Uzbekistan 


0.687 


19.885 


167.13 


155 


321.98 


Albania 


0.784 


86.530 


359.23 


847 


1206.09 


Bosnia Herzegovina 


0.803 


204.069 


-187.67 


-1194 


-1381.94 


Bulgaria 


0.803 


154.375 


545.27 


991 


1536.01 


Croatia 


0.817 


-260.356 


1069.15 


1552 


2620.74 


Czech Republic 


0.847 


-193.848 


804.44 


2257 


3061.60 


Germany 


0.896 


609.839 


1742.87 


6119 


7862.04 


Hungary 


0.812 


488.092 


658.09 


1976 


2633.60 


Macedonia 


0.782 


54.117 


54.07 


115 


169.31 


Mongolia 


0.676 


43.536 


188.90 


239 


428.13 


Montenegro 


0.815 


67.825 


585.22 


890 


1475.08 


Poland 


0.806 


407.315 


1053.42 


3132 


4185.24 


Romania 


0.786 


112.694 


515.12 


949 


1464.24 


SerbiaQ 


0.797 


-224.142 


-210.83 


-1117 


-1327.54 


Slovakia 


0.827 


91.391 


905.71 


3381 


4286.55 


Slovenia 


0.853 


452.178 


1956.23 


5472 


7427.85 


Average 


0.780 


106.415 


605.63 


1218.49 


1824.12 


Standard Deviation 


0.058 


248.685 


558.00 


1702.22 


2225.02 


Minimum 


0.058 


-260.356 


-210.83 


-1194.27 


-1381.94 


Maximum 


0.896 


899.312 


1956.23 


6119.18 


7862.04 



231 



Figure 5 Shadow Economy MIMIC Diagram. 



Business 
Regulations 



Unemploy- 
ment Rate 



Transfers and 
Subsidies 



Government 
Consumption 



: ~~~~^fc' Shadow \/_r 




Labor Force 
Participation 





Real GDP per 
Capita 





— *■ 


Bribes 





Judicial 
Independence 



Figure 5 Shadow Economy MIMIC Model (Schneider, et al., 2010; Buehn, et al., 2009 Figure 3. Path 
Diagram; Breusch, Trevor, 2005). 



Shadow Economy = / ( p x + $ 2 X 2t + P 3 *3t + P 4 *4t + P 5 *5t + \k ). and 


Shadow Economy = / ( P x + P 6 * 6 t + $ ? X 7t + P 8 X 8t + u t ) 


Where causal variables are 


Where Indicator variables are 


X 2 - Business Regulation 


X 6 = GDP Growth 


X 3 = Uunemployment Rate 


X 7 = Labor Force Participation 


X 4 = Transfers and Subsidies 


X 8 = Ratio of M0 to Ml 


X 5 = Government Consumption 




Corruption = / ( p\+ P 1Q X 10t + P 1± X llt + $ 12 X 12t + $ 13 X 13t + u t ), and 


Corruption = / ( p\+ P 14 X 14t + P 15 ^i 5 t + P 16 *i6t + Ut ) 


Where causal variables are 


Where indicator variables are 


X 12 = Government Effectiveness 


X 16 = Real GDP per Capita 


X 13 = Fiscal Freedom 


X 17 = Bribes 


X 14 - Bureaucracy Costs 


X 18 - Judicial Independence 


X 15 - Rule of Law 





Figure 6 Shadow Economy Simultaneous Equations. 



232 



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UN 








oo 




u 


%o 


Os 








g 






■i 




i 


*r 


V( J 


L/l 


l/l 


vT.i 


rTl 


' ' 


1/1 


'I 


■ ■ 


i 




un 




M 1 


r - H 






KO 




rTl 


M 1 


■1 


i 


t> 






rfl 




00 ^1 




























fN 


' ' 


fN 




.-, 


fN 






■Tl 




en 


'"1 




'' 


' 1 






' ' 


■ ' 


en 




r-| '"' 


« 


en 


oo 


o\ ^H 


: : 


r"l 




n 














































■ 






















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1 w 


o 


' 1 


un 


■ 1 


00 


( i 


■-:.' 


"1 


■■T.' 


I 


,-^ 


''■ 1 


V i 


"i 


'■'■;' 


■1 


O 


'1 


Os 


■ i 


<Xl 


i 


Q 


00 


'O 


'O 


■■T.' 


W i 


00 




■T.i 


00 


en 


■U' 


r | 


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1 1 










■ ■ 




■ ■ 






■ ■ 




r--. 




fn 










VI 


■ ■ 




■o 


TOJ 


OO 




yj 






oo 






O 




■o 






UN 






en 


OO 


•■> i 














<ft 




iJn 








[~M 
















M I 








en 




























■iO 






UN 


<^i 
























































^r 










en 








UJ 






(h 


■O 




(ft 




~H 




















































































u 






































































o 














1 1 






•' i 


UN 


, i 


TO) 


■-t.i 


iJn 


( | 


OO 


llN 


. 1 


■ ■ 


r | 


■T.i 


i 


' i 


, , 


■■■ , 


,/: 




■ i 


"■■i 




fn 


oo 


en 


■1 


■i: 


^T.i 


UN 






■::■ 


( 1 


OO 








i 


to> 


■' i 




i in 


■■ ■■ 




"i 


i'J 






CO 


n\ 


v> 






,■;, 


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sr> 












■: :■ 


fn 










ON 






UN 






■ft 












m 




V I 






■ in 


OO 


"I 




■sT.i 


fn 




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■ i 


VI 






^o 


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Os 




r^l 






oo 








f>. 










, i 




fN 








fN 


» ' 


fN 




, i 


fN 






■ ■ 




en 


I-. 






■ i 


■ i 






r-i 


- ■■ 








fN 


Oi 


en 


1 


UN 


\y 






o 
H 






































































fN 


s 














i 




f | 


i 


llN 


i-n 


1 


rTl 


" 1 


. i 


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•' i 




,■ i 


i-n 


- i 


fN 


V~l 


en 




. i 


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VI 


."1 


V~l 


■■T.i 




fN 


■: ;■ 




■ 1 


■::• 




CO 


."i 












OO 


fS 




V I 




'■^.i 


'■■T.i 




'T.i 






CO 




i'Jn 


en 






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Os 


■M I 




















oo 






(1 










CO 


■ 1 










on 


WJ 


■ i 






■i 


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fN 


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en 


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■i 


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VI 


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1 


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CO 


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1 




fN 


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< i^B-t- 


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Vi 










rfl 




UN 


UN 




■ i 




r-i H 






■ ■ 




CO 


' 1 










1 Os 








'/.I 


'■■T.i 




■: :■ 


i In nO ■ ■ 


■ 




























CO 


■■> 1 




Os 


■1 O 






I-. 




V, 




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e*l ^H ~r 


■ 












Vi 










Vi 














ON ■ 






i in 


so 


■::■ 


Os 




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f= 








1 1 PH 






i/i 


^ 




iv) 


■ 1 




"I 




r-, 


( | 


i 




^ 


•' i 




■o 




rr-, 










UN 




On 


en 


•< 1 


■i 


,- 1 


I 




UN 


1 


r | 


^^ 


, , 


"i 


i | 










TO) 




On 


On 




^o 






o\ 


i in 










VI 












"l 










■ ■ 


oo 


UN 






UN 


■■ .■ 






■T.i 


■ i 






VD 










■j-i 


■o 




■"■ 






























u 


Vl 




l-l 






',. i 




O 


On 


TO) 
































-r 






-r 












V 


VI 






( 1 








OO 








0s 


UN 


^T.i 


■. :■ 


oo 






^N 


3 






































re 


















■ ' 




JB 




■ ' 




, , 


" ' 


■ ' 








5 






























e-: 


1 








r, 






FO 
















IB 














1 1 




(J 




































































































































































Pi 






WO 






N 






g 
| 

1 

I ' 














i/i 














rJ5 


u 

.=1 


T.I 
,J=I 


s 
















fj 


















re 

& 

o 




£ 
o 

Uh 


W 
1 ' 


it; 

O 
■r.J 


1 

!■■'. 


3 


CI 
O 
■1.' 

O 


■r.J 

II, 

1 


| 

Is 

■JJ 


n 
j 

■s 


. 1 


^2 


in 


IT! 

i 


1 

■1' 

m 


3 

T.I 
111 


cm 
'*" 

1 

o 

Uh 


rt 
■iv 

■VJ 


O 

■lv 




s 


1 

o 

U 


o 


I 

m 


u 

O 
Uh 


u, 

& 

1 

■".' 
T7? 


■V 

N 




l/l 
UJ 

o 

Uh 


■■_! 


it; 


it; 




1 




■r.J 

3. 

<■-• 


o 

f 
o 

Uh 


1^ 


1 

n, 


U 

1,' 
O 


J 


u' 


1 
I 




P 



233 



Table 7. 1 Data Validation. 





!-!:.■■;; Z -. -..:. :-.-. ■ '.. :-/: 


BmaWn* 


ar:;:;: Ch:;; -::rj=E::: 5f:iji:" 


1 


a 




| 


1 


| 


? 


| 


i 


I 


I 


| 


I 


9 


n — 

6 1 


1 


I 


Alfl 


Cc-fe 


Ccunnv 


HDI90 


HDIOS 


CHHDI 


POP 90 


POP OS 


C-PO? 


era> » 


I3DP03 


c-:e? 


3; 53 


: : :s 


DCr-Z: 


CHfc 


A-.=Q-c3 
:" 




ALB 


Albania 


8. "8: 


tutu 


10*34 


329 


.-.:- 


AOM 


3.2163-: 


5.B532 


C. "S3 


5"S 


:S25 


8:" 


CS" 


:.8":28 


■_ 


■ipjy 


Armenia 


0.731 


1798 


COS!" 


3.54 


3.08 


-c:3: 




;:;;; 


-.T55- 


:.c35 


"35 


123:> 


558 


8. S3 




:::;; 




AUT 


AQSfaB 


8.S9S 


0955 


10623 


7.71 


S.3- 


1011 


:-5 


:.<:.::« 


22".:ss:- 


C525 


: 532: 


2"25: 


7927 


141 




■_,-: 


- 


AZE 


Azerbaijan 


0.755 


1787 


8.842- 


7.16 


S.6S 


c::: 


g 


55-C5 


:s.-;;2S 


1.066 


1251 


2131 


SS8 


:.": 




.:>::; 




e:?. 


Belarus 


1795 


c.s:s 


10393 


:::.:; 


5.68 




;_ 


36^1? 


2-.3:"2S 


3.S5 


;:;: 


25:5 


::s5 


178 




.8^:3c 




Bi-: 


3c=nk andHerzescvina 


8.S83 


1812 


:.:::: 


-.. : : 


177 


-C.:2- 


i- 


":;:: 




-C.-3C 


:-5 


2:c2 


-;- 






.:"":- 


7 


1ST. 


Bulgaria 


MB 


IS* 


8.846": 


s.~: 


7.62 


-;..::s 


:_ 


5S-23 


:c:ss3s 


3.353 


:r: 


2c a 


;;: 


0.59 




. : _~ _~ - _~ 


t 


HRV 


Croatia 


0.817 


cs _ : 


:.xt: 


4"S 


i 1- 


-C.C"2 


a 


::S5: 


3c:sx^ 


3.232 


5255 


6S8" 


:552 


8.38 


:.::":8 


; 


C'f? 


CvprUi 


0.849 


1914 


38" oc 


C6S 


OM 


c:r 


; 


:;:." 


:2.3ee:5 


3. 585 


:ces- 


15510 


4326 


145 




:-. : ": 




CZE 


Cze:hF^p±ik: 


8.S4" 


1903 


10661 


1036 


:::.-: 


C.X 6 




"'53-" 


"S.:55:: 


3.-3: 


5336 


^596 


2257 


8.:2 




..,.,.. 




ESI 


Ei:orda 


S17 


1883 


:.:;:; 


157 


:.-- 


-c:-s 


5 


5S"3: 


5.53": 2 


3.55; 


3S22 


7114 


825: 


CSS 




.:;:-; 




-\" 


Rfetxl 


:.?:- 


J95S 


10608 


499 


:■.;: 


1066 


s 


3::.:^ 


:f3. — gjic 


3.5-5 


:5S:c 


285:; 


5325 


" : r 




.84582 




C-EC 


Georgia 


:..".;; 


1T78 


10528 


546 


4.31 


-:,:: : 


S 


:5:5: 


147 52 c 


■ft 328 


1572 


:2:5 


-323 


-22: 




.5-53 


■_ 


DEU 


Germanv 


8.S56" 


:,=-" 


3.8565 


"5.-3 


8211 


CC3- 


■ ^3 


. ., . r 


2.C5".6"5^CS 


3.355 


:s-2S 


255^" 


c::s 


8.3: 




.882— 


15 


c-?.8 


Gee:e 


:.s": 


3.543 


10303 


:c:s 


::.:- 


:.:c6 


m 


;::;: 


:"CS3S^5 


0.715 


5383 


'-"--■ 


5403 


8.55 




.85:52 




EU3\" 


riaiEarv 


C.S2 


1879 


10825 


- :- 


:CC4 


-CS32 


43 


5SS55 


C'2.355c2 


:.-:s 


:2-8 


g:c 


■;-■■■ 


" :" 




.84682 


;- 


"A 


fc^ 


C.SS9 


1951 


10697 


5672 


5983 


0.055 


53" 


555 S3 


:.:":. S"5S5 


C253 


16531 


:558c 


m« 


118 




.:::*: 


/. 


: . .-^ 


Xazatliitan 


D.77B 


ess: 


8.833: 


:c.85 


1567 


-cc^: 


26 


^"'S 


3" .335-5 


;.-:f 


:c":2 


1388 


768 


148 




.::5. : 5 


:s 


vez 


K^lgysfaQ 


8.6S" 


;.":; 


10335 


442 


5.:s 


c:?3 




c^s 


2.CCC5: 




^ s 5 


3 "5 


-Sc 


-c:s 


8.5c""c 




LVA 


Lafttm 




C.SS6 


::.s"S5 


2 g" 


227 






-:=5: 


:3.rs-3 




35: : 


68 36" 


2135 




:.;«""2 







Lithuania 


:.s:s 


CS"S 


00507 


3. "8 


3 36 


-CCS2 




sscc: 


-,- -,„-; 


3.2"" 


-:-: 


5:5: 


:::.- 


323 


:.8-8:8 




XEO 


ftfte edonia 


D.7B2 


cs:" 


8.844S 


:.;: 


2.8- 


CC5? 




532 :f 


-.-. : 52. : 


0.129 


:;:; 


2:58 


285 


::.:2 


:.::::: 




!JA 


.■-ikiova 


1735 


:.":;: 


-8.8204 


436 


363 


-:..:r 




e:s:5 


. ,:::.5 


4.417 


588 


55: 


-385 


." :" 


0.55 60S 


2- 


d*JG 


-■.rnsosa 


8.6"6 


1727 


8.8^54 


222 


:.s- 


:.:;: 




:95-2 


:. 5:2:5 


0.767 


:5 c 


"35 


285 


8.^ 


:.:3-"8c 


a 


:.:-E 


.vimenesro 


C.S15 


1134 


: .8233 


159 


162 


:.:■;: 




s-is :f 


:.-53C5 


0.713 


:-5 


2335 


858 


8.c2 


:.8S26: 




?c: 


Poland 


s.sse" 


1880 


C05:S 


3s.:: 


3813 


:.:■:: 


118 


:-: _ : 


23". ^c 525 




3097 


6228 


8:82 


:.:: 


:.8^28^ 


a 


?.c: : 


F.ornania 


8."S6 


WB1 


;:.;«-; 


:.;.:: 


::.5: 


-0.073 


43 


mi: 


s:.:5S55 


:.35: 


:S5c 


28:5 


;~; 


8.58 


:.C:c^S 




R.U5 


Fjjss ian 7e dera lion 


:.;:: 


1817 


-:.:■:-; 


:-s.2S 


:~:.?5 


-c;^3 


3g^ 


§p~<~* 


-32.C236: 


:.:2: 


2602 


33:8 


--. 


0.17 


: . : : ~:c 


a 


3?Z. 


Serbia*- 


:.."r 


c.s:s 


C.53&i 


".:; 


5:5 


13M 


1C.S-6C 


2.555": 


-0 72^ 


:-5 


:2c2 


-IB 


-c:3 


:.535:: 




: .-■. 


SLralfla 


8.S2" 


esse 


cm-: 


528 


r -\ 


■ --■: 


27 52753 


^c.-5Cc3 


3.SS" 


52:: 


855: 


33 s: 


165 


: . :c-68 




5VN 


Slovenia 


DJ53 


1929 


10891 


2.03 


202 


cc: 


:s\e:s5" 


-,- ^- . -_ 


0.677 


8317 


:3"S5 


3: "2 


S.cc 


:.8:c"5: 


" 


.. J\ 


Tajiliitan 


8.636 


C.SSS 


:.:;:; 


5.3;: 


684 


1289 


225952 


:.c~-55 


-3.255 


:2 c 


Vi 


-:s: 


." 1- 


:.JJ-;; 




H3& 


HEkey 


0.705 


CSC6 


c:-33 


5c.CS 


73.91 


1318 


:ss c-' "' 


3" 5. 3 "-2 


1.010 


3328 


53": 


:":" 


8.52 


1.05443 


:_ 


HO.I 


Tu:i::r.i:1-;:;:". 


: 8: 


: ~. ; ; 


c::::3 


3.67 


5.84 


0.375 




;::.5: 


S.c^52" 


1263 


::-2 


-■- 


g72 


065 


■ -;■; 




UKK 


~Jl:raine 


8. "54 


1796 


0.0557 


5:.ss 


4626 


-0.109 


71 


5:3 35 


53. -c "3" 


-3.25" 


1387 


::5c 


-23: 


-0.17 


0.562SS 


;; 


UEB 


Oztjeksbn 


s.ss - 


:.":: 


10335 


2051 


2~.3: 


3332 


■ 1 


C^3S5 


22.53 :S: 


- .;:: 


6S5 


s^" 


155 


123 


:.8232c 



234 



Table 7. 1 Data Validation (Continued) 





■if::ii:""E; : :: : :"' i : i :'.^i: ::; ::".>:■ ■= ■::: i:: : = .': ■:■.-?::::: 


TosI&eee 3::tee::: "■::1^:: 1 


-z. 


1 




* 


1 




1 


-3 

■_ ■_- 


:S7 

| 3 


:S7 

1 § 


3j g 

J J 


7: 

.6 't 


j 


-3 
•7: 


If 


E^ "^ 


-— -~1 


Sft 


1 


Crantry 


SE90 C 


SE590 


SEOS 7 


SESOS 


CHSE% 


SEfcSO 


SBcOJ 


SCH:E : 


CHSE-c 


5GD2 9S 


3GD2 3S 


CH3GD? 


31=53 


31= 03 


SCH3"1I= 


CH3i 


Albania 


SIjQD 


i 


MM 


35.33 




2.SS1S 


0.171 


303 


662 


359 


1.1s 


4.21 


7.32 


3.36 


1231 


24S- 


1236 


5.512 


Armenia. 


40.30 




1.1362 


4a "3 




2.2 


3.233 


236 


632 


347 


1.21 


3.56 


6.95 


0.76 


995 


1551 


536 


5.912 


An itria 


7.CC 




lfl.430S 


14.65 




55.1655 


1.336 


1353 


55 "5 


2626 


1.54 


15545 


265.36 


0.63 


236" 6 


51225 


13555 


3.513 


Azerbaijan 






4.0SB 


5S ^3 




11." 133 


0.404 


:64 


1515 


"35 


1.3S 


12.99 


33.21 


1.55 


1315 


5133 


1665 


5.913 


Bslani* 


35. 6C 




5. 113. 


-5.33 




12.12-5 


3.355 


532 


1252 


751 


1.5C 


15.43 


36.4" 


0.37 


1512 


376" 


1356 


6.571 


3 tsnia anc Becsegtn&H 


13.00 


i 


4.122; 


54.65 




2.532- 


3.236 


405 


743 


344 


3.3: 


13.35 


11.29 


-0.40 


1353 


2s-3 




C.574 


Btlsaria 


27. 1C 




3iMS9 


3". 53 




~ 613 


3.531 


453 


553 


545 


1.2C 


13.51 


2795 


0.51 


2123 


3655 


1556 


C."23 


Cnsta 


HfiD 




6.1792 


34 "0 




104 "2: 


3411 


1255 


2362 


1065 


0.33 


51.30 


^.il 


0.50 


6:"iS 


5163 


2521 


0.150 


CvpiLi 


a.0D 




1.3011 


25.43 




3 6162 


1400 


2244 


1563 


2^6 


1.05 


".50 


15.52 


1.12 


1252" 


20055 


"111 


0.552 


CzKhRsptblk 


13. 10 




".2--L 


15 31 




15 6727 


0.311 


655 


1:03 


30+ 




62.;i 


51.35 


0.52 


603: 


xr 


5062 


0307 


Estonia 


H3G 




2.0571 


*£.iZ 




3 3435 


3.1"5 


1511 


236" 


1556 


1.1s 


3. 35 


15.33 


0.66 


5133 


5533 


IS-" 


0.911 


Finland 


L-.:T 




14,353- 


IS 50 




23 -43" 


3.2"6 


2333 


5351 


2466 


3"- 


113. "3 


132.23 


0.65 


22334 


31253 


_-4S_ 


6.561 


Of azia 


-5.10 




3 6763 


63.33 




3 7670 


3.525 


"35 


St 




3.21 


11.33 


5.21 


-0.22 


2233 


2103 


-1~2 


-6.6"5 


Serantg 


::.:: 




133.2" C2 


1610 




55" "225 


3.323 


2370 


4113 


l"4-3 


S.-i 


1731.4" 


21 S3 ^3 


041 


2'~SS 


25665 


"362 


6.361 


Sse&BB 


25. "3 




23. 65 "6 


25.53 




51.333" 


3.262 


2325 


1546 


2222 


3.56 


123.22 


221.52 


1 30 


1212" 


19-15 


"622 


6.625 


HiMiEar, 


2130 




S.S5S5 


25 30 




16.3531 


0.13" 


S^l 


1634 


653 


0.70 


55.33 


"345 


0.16 


5136 


"323 


2631 


6.563 


Itah 


25.16 




219.3974 






513.-515 


3.162 


3363 


552" 


1455 


E.3S 


1156.99 


1495.63 


0.25 


23555 


2i51i 


4515 


6.221 


Kazaldiilan 


31.55 




3.4531 


45.33 




16.35*1 


3.423 


514 


IS "3 


564 


1.M 


34.75 


54.23 


5.56 


2126 


3453 


1332 


6.62" 


K\TH\:Etan 


ii?.2C 




ansa 


-2.33 




5 3-52 


3.153 


164 


155 


-4 


-3.33 


2.73 


2.S4 


0.02 


625 


553 


-90 


-6.144 


La--ia 


25. "6 




2 6 — 


41. "3 




5. "355 


3.625 


1305 


251" 


1511 


1.51 


i: :: 


15.53 


0.43 


:.;:- 


3335 


5645 


6. "44 


Lithuania 


2S.0C 




4.12:3 


31.53 




6.-61- 


0.227 


10:1 


1644 


554 


3.56 


19.99 


26. "2 


0.34 


5591 


6-SS 


1707 


5.335 


Macaaonia 


35.60 




1.3553 


36.23 




1.63-3 


0.01" 


633 


"31 


S3 


0.14 


5.33 


6.35 


0.13 


2652 


2513 


337 


6.136 


Mckicva 


2930 




1.060B 


-5.83 




3.5665 


0.563 


237 


271 


-16 


-0.06 


1.63 


5.33 


-0.34 


1267 


362 


-405 


-6.326 


Moegda 


is. is 


■ 


D.19S0 


3". 53 




3. "561 


1.351 


S3 


2 "5 


135 


110 


1.35 


2.63 


1.06 


5 36 


1311 


123 


6. "51 


IfcofettEKi 


2S.S0 




C.CC2C 


35.6" 




3. 5 "64 


0.631 


341 


526 


33: 


1.72 


0.3: 


2.03 


1.39 


1"S6 


3261 


14"5 


0.326 


Pdarei 


2130 




25.3231 


23 ZZ 




664535 


0.256 


651 


l"-i 


1353 


1.53 


111.36 


303.56 


1.11 


3" 3" 


-S"2 


i'S- 


1.105 


Romania 


2~.30 




12.CC92 


56.53 




22 2-- 


0.330 


517 


1355 


515 


LflO 


56.00 


33.41 


049 


2115 


5S~ 


1151 


0.65" 


Rtiiian F=i=iaticr. 


27 go 




0" "'"?' 


-3.63 




235.563: 


3. "IS 


"25 


li "5 


"56 


1.04 


155. 1" 


611.95 


0.50 


5526 


1325 


115" 


6.566 


:etbia 


23.SD 




2.336: 


35.6" 




1.1 XC 


0.631 


S41 


501 


160 


O.i" 


13.3: 


1.15 


-0.65 


1"S6 


1"55 


-25 


-0.013 


Sloiafea 


15.10 




4.1547 


15. "3 




5.1533 


3.335 


"3" 


1655 


536 


LIS 


31.63 


55.65 


0.75 


555" 


13231 


123" 


0."15 


: btnsa 


::.?: 




3J057 


23. 3C 




" 3045 


0.125 


IMS 


3361 


15:6 


1.03 


20.12 


33.63 


0.": 


10222 


1"6:0 


"i2S 


0."2" 


HEgiataa 


24.00 




3.5-25 


44.53 




3. "-IS 


3.316 


132 


105 


6 


0.06 


2. S3 


2.-2 


■ 0.1- 


523 


353 


-175 


■ 0.331 


_i:d;=\ 


2O50 


£ 


53.2515 


52.53 


E 


123.3554 


3.635 


632 


1665 


537 


1.4: 


221.95 


153.1" 


1.22 


4513 


r— 


2754 


6.532 


jidcmsnis Ian 


24.00 




s.si"s 


: 6 ZZ 


1 


3.1125 


3.500 


253 


61" 


^6" 


1.4" 


1. 1 


11. "6 


1.43 


1292 


2551 


1355 


6.335 


Ukraine 


29.40 




21.13-3 


53.50 




23.3 1SS 


0.333 


413 


623 


215 


0.53 


53.11 


32.25 


-0.12 


l"5i 


. 5 


-15 


-6. 60 5 


Uzb=ki=. tan 






3.1035 


3753 


y 


3.6533 


3. "16 


151 


313 


16" 


lie 


1".15 


31.63 


o.s- 


336 


1153 


522 


6.535 



235 



Table 7.1 Data Validation (Continued) 





Life hxpectancv^ 


Adult Literacy 


E du£ ational Attainment Index 


1 

-z. 


g 


5 


-i 


^ 


^ 


| 


z 


1 


3 


| 


1 


Dfl 


Country 


LE90 


LE07 


CHLE 


LET 90 


LET 07 


CHLEI 


AL90 


AL07 


CHAL 


E.AI90 


E.AI07 


CHEI 


Albania 


71.9 


76.5 


0.064 


0.782 


0.858 


0.098 


85.0 


99.0 


0.165 


2.14 


0.89 


1.726 


.Armenia 


67.9 


73.6 


0.084 


0.715 


0.810 


0.133 


93.0 


99.5 


0.070 


2.25 


0.91 


1.846 


.Austria 


75.fi 


79.9 


0.057 


0.844 


0.915 


0.084 


99.0 


99.0 


0.000 


2.90 


0.96 


2.568 


Azerbaijan 


65.fi 


70.0 


0.066 


0.677 


0.751 


0.108 


93.0 


99.5 


0.070 


2.25 


0.88 


1.858 


Belarus 


70.fi 


69.0 


-0.023 


0.760 


0.733 


-0.035 


95.0 


99.7 


0.049 


2.47 


0.96 


2.081 


Bosnia and Herzegovina 


66.7 




5 1 


0.125 


0.696 


0.834 


0.199 


92.7 


96.7 


0.043 


2.34 


0.87 


1.966 


Bulgaria 


71.2 




3.1 


0.026 


0.770 


0.802 


0.041 


93.0 


98.3 


0.057 


2.42 


0.93 


2.036 


Croatia 


71.9 




6.0 


0.056 


0.782 


0.850 


0.087 


92.7 


98.7 


0.065 


2.34 


0.92 


1.949 


Cyprus 


76.5 


', 


9.6 


0.040 


0.859 


0.910 


0.060 


87.0 


97.7 


0123 


2.27 


0.91 


1.869 


Czech Republic 


72.1 




6.4 


0.060 


0.785 


0.856 


0.091 


97.0 


99.0 


0.021 


2.68 


0.94 


2.330 


Estonia 


(59.4 




2.9 


0.051 


0.740 


0.799 


0.080 


96.0 


99.8 


0.040 


2.66 


0.96 


2.298 


Finland 


70.5 


- 


1 6 


0.015 


0.759 


0.777 


0.024 


93.0 


99.0 


0.065 


2.25 


0.92 


1.843 


Georgia 


75.5 




9 5 


0.053 


0.837 


0.908 


0.085 


99.0 


99.0 


0.000 


2.86 


0.93 


2.534 


Germany 


75.5 




93 


0.057 


0.842 


0.913 


0.084 


99.0 


99.0 


0.000 


2.90 


0.95 


2.571 


Greece 


77.2 




9 1 


0.025 


0.869 


0.902 


0.037 


93.2 


97.1 


O042 


2.41 


0.98 


2.003 


Hungary 


69.4 




3.3 


0.056 


0.740 


0.805 


0.088 


97.0 


98.9 


0.020 


2.73 


0.96 


2.378 


Italy 


76.9 


81.1 


0.055 


0.864 


0.935 


0.082 


97.1 


98.9 


0.019 


2.54 


0.97 


2.160 


Kazakhstan 


66.7 


64.9 


-0.027 


0.696 


0.666 


-0.043 


93.0 


99.6 


0.071 


2.25 


0.97 


1.821 


Kyrgyzstan 


66.3 


67.6 


0.020 


0.688 


0.710 


0.033 


93.0 


99.3 


0.068 


2.25 


0.92 


1.842 


Latvia 


69.1 


72.3 


0.047 


0.734 


0.788 


0.073 


96.0 


99. S 


0.040 


2.66 


0.96 


2.299 


Lithuania 


70. S 


71.8 


0.014 


0.763 


0.780 


0.022 


96.0 


99.7 


0.039 


2.66 


0.97 


2.296 


Macedonia 


71.4 


74.1 


0.038 


0.773 


0.819 


0.059 


92.7 


97.0 


0.046 


2.34 


0.88 


1.964 


Moldova 


67.fi 


6S.3 


0.011 


0.709 


0.722 


0.018 


95.0 


99.2 


0.044 


2.38 


0.90 


2.002 


Mongolia 


60. S 


66.2 


0.089 


0.596 


0.687 


0.152 


93.0 


97.3 


0.046 


2.42 


0.91 


2.043 


Montenegro 


75.fi 


74.0 


-0.021 


0.843 


0.817 


-0.030 


92.3 


96.4 


0:045 


2.34 


0.89 


1.959 


Poland 


71.1 


75.5 


0.061 


0.769 


0.842 


0.095 


96.0 


99.3 


0.034 


2.57 


0.95 


2.200 


Romania 


69.4 


72.5 


0.045 


0.740 


0.792 


0.070 


95.0 


97.6 


0.027 


2.47 


0.92 


2.100 


Russian federation 


67.9 


66.2 


-0.025 


0.714 


0.686 


-0.039 


94.0 


99.5 


0.059 


2.61 


0.93 


2.253 


Serbia 


71.fi 


73.9 


0.033 


0.776 


0.816 


0.051 


92.3 


96.4 


0.045 


2.34 


0.89 


1.959 


Slovakia 


71.fi 


74.6 


0.043 


0.776 


0.827 


0.065 


97.0 


99.0 


0.021 


2.72 


0.93 


2.379 


Slovenia 


73.1 


78.2 


0.070 


0.801 


0.886 


. 1 06 


92.7 


99.7 


O076 


2.34 


0.97 


1.926 


Tajikistan 


62.9 


66.4 


0.056 


0.632 


0.691 


0.093 


93.0 


99.6 


0.071 


2.25 


0.90 


1.852 


Turkey 


64.fi 


71.7 


0.109 


0.660 


0.779 


0.179 


80.7 


88.7 


0.099 


1.82 


0.83 


1.365 


Turkmenistan 


62. S 


64.6 


0.029 


0.629 


0.661 


0.050 


93.0 


99.5 


0.070 


2.25 


0.91 


1.847 


Ukraine 


69.7 


68.2 


-0.022 


0.745 


0.720 


-0.034 


93.0 


99.7 


0.072 


2.30 


0.96 


1.883 


Uzbekistan 


66. S 


67.6 


0.012 


0.697 


0.711 


0.019 


93.0 


96.9 


0.042 


2.25 


0.89 


1.855 



236 



Table 7.1 Data Validation (Continued) 





life hxpectancv" 


Adult Literacy 


E due ational Attainment Index 


z 


>-, 


1 


jjj 


| 


1 


■!>] 


>-. 


| 


| 


g 


g 


1L 
3 ^ 


Country 


LE90 


LE07 


CHLE 


LEI 90 


LEI 07 


CHLEI 


AL90 


AL07 


CHAL 


EAI90 


EAI07 


CHEI 


.Albania 


71.9 


76.5 


0.064 


0.7 82 


0.S5S 


0.09S 


85.0 


99.0 


0.165 


2.14 


0.89 


1.726 


.Armenia 


67.9 


73.6 


0.0S4 


0.715 


0.810 


0.133 


93.0 


99.5 


0.070 


2.25 


0.91 


1.846 


.Austria 


75.6 


79.9 


0.057 


0.S44 


0.915 


0.084 


99.0 


99.0 


0.000 


2.90 


0.96 


2.568 


Azerbaijan 


65.6 


70.0 


0.066 


0.677 


0.751 


0.108 


93.0 


99.5 


0.070 


2.25 


0.88 


1.858 


Belarus 


70.6 


69.0 


-0.023 


0.760 


0.733 


-0.035 


95.0 


99.7 


0.049 


2.47 


0.96 


2.081 


Bosnia and Herzegovina 


66.7 




5 1 


0.125 


0.696 


0.834 


0.199 


92.7 


96.7 


0.043 


2.34 


0.87 


1.966 


Bulgaria 


71.2 




3 1 


0.026 


0.770 


0.802 


0.041 


93.0 


98.3 


0.057 


2.42 


0.93 


2.036 


Croatia 


71.9 




6.0 


0.056 


0.782 


0.850 


0.087 


92.7 


98.7 


0.065 


2.34 


0.92 


1.949 


Cyprus 


76.5 


- 


9 6 


0.040 


0.S59 


0.910 


0.060 


87.0 


97.7 


0.123 


2.27 


0.91 


1.869 


Czech Republic 


72.1 


- 


6.4 


0.060 


0.785 


0.856 


0.091 


97.0 


99.0 


0.021 


2.68 


0.94 


2.330 


Estonia 


69.4 


1 


2.9 


0.051 


0.740 


0.799 


0.080 


96.0 


99.8 


0.040 


2.66 


0.96 


2.298 


Finland 


70.5 


- 


1 6 


0.015 


0.759 


0.777 


0.024 


93.0 


99.0 


0.065 


2.25 


0.92 


1.843 


Georgia 


75.5 


- 


9: 


0.053 


0.837 


0.908 


0.085 


99.0 


99.0 


0.000 


2.86 


0.93 


2.534 


Germany 


75.5 




93 


0.057 


0.842 


0.913 


0.084 


99.0 


99.0 


0.000 


2.90 


0.95 


2.571 


Greece 


77.2 




9 1 


0.025 


0.S69 


0.902 


0.037 


93.2 


97.1 


0.042 


2.41 


0.98 


2.003 


Hungary 


69.4 


- 


1 3 


0.056 


0.740 


0.805 


0.088 


97.0 


98.9 


0.020 


2.73 


0.96 


2.378 


Italy 


76.9 


Sl.l 


0.055 


0.S64 


0.935 


0.082 


97.1 


98.9 


0.019 


2.54 


0.97 


2.160 


Kazakhstan 


66.7 


64.9 


-0.027 


0.696 


0.666 


-0.043 


93.0 


99.6 


0.071 


2.25 


0.97 


1.821 


Kyrgyzstan 


66.3 


67.6 


0.020 


0.688 


0.710 


0.033 


93.0 


99.3 


0.068 


2.25 


0.92 


1.842 


Latvia 


69.1 


72.3 


0.047 


0.734 


0.788 


0.073 


96.0 


99.8 


0.040 


2.66 


0.96 


2.299 


Lithuania 


70.S 


71.8 


0.014 


0.763 


0.780 


0.022 


96.0 


99.7 


0.039 


2.66 


0.97 


2.296 


Macedonia 


71.4 


74.1 


0.038 


0.773 


0.819 


0.059 


92.7 


97.0 


0.046 


2.34 


0.88 


1.964 


Moldova 


67.6 


6S.3 


0.011 


0.709 


0.722 


0.018 


95.0 


99.2 


0.044 


2.38 


0.90 


2.002 


Mongolia 


60.8 


66.2 


0.0S9 


0.596 


0.687 


0.152 


93.0 


97.3 


0.046 


2.42 


0.91 


2.043 


Montenegro 


75.6 


74.0 


-0.021 


0.843 


0.817 


-0.030 


92.3 


96.4 


0.045 


2.34 


0.89 


1.959 


Poland 


71.1 


75.5 


0.061 


0.769 


0.842 


0.095 


96.0 


99.3 


0.034 


2.57 


0.95 


2.200 


Romania 


69.4 


72.5 


0.045 


0.740 


0.792 


0.070 


95.0 


97.6 


0.027 


2.47 


0.92 


2.100 


Russian Federation 


67.9 


66.2 


-0.025 


0.714 


0.686 


-0.039 


94.0 


99.5 


0.059 


2.61 


0.93 


2.253 


Serbia 


71.6 


73.9 


0.033 


0.776 


0.816 


0.051 


92.3 


96.4 


0.045 


2.34 


0.89 


1.959 


Slovakia 


71.6 


74.6 


0.043 


0.776 


0.827 


0.065 


97.0 


99.0 


0.021 


2.72 


0.93 


2.379 


Slovenia 


73.1 


7S.2 


0.070 


0.801 


0.88(5 


0.10(5 


92.7 


99.7 


0.076 


2.34 


0.97 


1.926 


Tajikistan 


62.9 


66.4 


0.056 


0.632 


0.691 


0.093 


93.0 


99.6 


0.071 


2.25 


0.90 


1.852 


Turkey 


64.6 


71.7 


0.109 


0.660 


0.779 


0.179 


80.7 


88.7 


0.099 


1.82 


0.83 


1.365 


Turkmenistan 


62.8 


64.6 


0.029 


0.629 


0.661 


0.050 


93.0 


99.5 


0.070 


2.25 


0.91 


1.847 


Ukraine 


69.7 


6S.2 


-0.022 


0.745 


0.720 


-0.034 


93.0 


99.7 


0.072 


2.30 


0.96 


1.883 


Uzbekistan 


66.S 


67.6 


0.012 


0.697 


0.711 


0.019 


93.0 


96.9 


0.042 


2.25 


0.89 


1.855 



237 



Table 7.1 Data Validation (Continued) 





.:■ i-./.-.v.i:/ l,: : : :-■. : -.: —m/.i-.-i-.i 




L=- - 

i 1 


X 

5 f± 


j 


^ 


£ ^ 
3 ^ 


5 = 

— D 
=£ = 


X 

^ - 
= a 
i r . 

o .a 
3 pj 


x 

H 
=. ^ 


§1 

- .= 

— x 
~ = 


^ £ 

=£ 


£ z 


± x 


■.= X 


Countrv 


:: s: ;i 


EE 0S H 


CHEE 


3E.E90 as 


D3E.Ec 
90 


D3E.E 
90 


3EESS as 


7J7II ; 
OS 


D3EE OS 


c:-:3eec 


dc:-:3ee 


dc:-:.^ee 

c 


CI-IAEE 
c 


.Albania 


10. S" 




S.43 




-0.22 


S.30 


139.23 


45 S 


6.19 


210 


659 


-0.254 


201 


71 


0,50" 


Armenia 


20.50 


V 


1120 




-0.36 


1-.61 


203. S" 


"23 


S.SS 


255 


"S5 


-0.392 


62 


51 


0.251 


.Austria 


8.13 




11.17 




0.3" 


".60 


16SC.50 


1295S 


S."5 


3-SS 


25CS5 


0.2S3 


1612" 


1SSS 


1.0"6 


.Azerbaijan 


26.66 




18.52 




-0.31 


1S.3S 


-S3.S" 


3-6- 


11.3- 


6-5 


5555 


-0.3S3 


2131 


161 


0.332 


Belarus 


17.10 


V 


11.26 




-0.34 


12.61 


326. S9 


3331 


".51 


424 


4105 


-0.404 


""- 


5" 


0.29" 


Bosnia and Hetzegevin 


1S."0 


o 


15.60 


I 


S).V 


14.61 


3-5. SS 


1490 


11.55 


454 


ri3 


-0.207 


223 


10S 


0.313 


Bulsaria 


SJ4 




9.26 




o.os 


6 72 


1S1.32 


15S1 


6.74 


339 


2584 


0.002 


1003 


15S 


0.S69 


Croatia 


is. - ; 


o 


5. SO 




-C.-S 


15.01 


122-.-1 


5S53 


".2S 


SSS 


3SS- 


-0.515 


-1S6S 


-326 


-0.266 


Cyprus 


13.17 




13.6- 




0.0- 


10. SS 


TC1.93 


115S 


10.5- 


2"3S 


2362 


-0.031 


1203 


1036 


0.609 


Czech Republic 


is.;: 


X 


5." 




-0.-2 


l-.S- 


1015.35 


1C5"0 


S.15 


SSS 


S263 


-0.-5- 


-130" 


-131 


-0.129 


Estonia 


25.50 


X 


U.7S 




-0.42 


1S.SS 


1305.03 


2054 


10.53 


1475 


1977 


-0.445 


-77 


166 


0.127 


Finland 


11.90 




12.59 




0.06 


10.3= 


2"13.6" 


13530 


10.63 


431S 


22946 


0.022 


5415 


1605 


0.591 


Georaa 


6.99 




10.11 




0.46 


4 76 


157.34 


S59 


5.SS 


213 


91S 


0.259 


55 


5 s 


0.354 


Germans 


sua 




10.01 




O.OS 


S.2S 


2005.40 


159295 


S.62 


256S 


2-3"12 


0.051 


S--16 


963 


O.-SC 


Greece 


6.55 




S.C5 




0.16 


5.61 


S-2.25 


S55S 


6.20 


1550 


1"S69 


0.104 


5311 


"-S 


O.SSS 


Hungary 


5.27 




11.50 




1. IS 


-.31 


2"3.31 


2S35 


5.1- 


SSS 


502" 


1.121 


6151 


626 


2.250 


Italy 


9.62 




9.51 




-0.01 


7.79 


1561.51 


1112"S 


7.4S 


2365 


141757 


-0.041 


3C-"5 


407 


0.20S 


j\.a:akhstan 


1S.SS 




1126 




-0.30 


1432 


401.46 


6563 


5.13 


459 


"ISO 


-0.362 


62" 


57 


0.1-3 


K T -i=r-"zstan 


2110 


V 


IS. 25 




-0.21 


17.09 


145.20 


642 


12. S5 


9S 


51S 


-0.24S 


-124 


-41 


-0.323 


Latvia 


16. S3 


V 


22.66 




0.35 


1.13" 


S23.S4 


2200 


15.55 


193S 


-353 


0.19" 


2152 


1115 


1.353 


Lithuania 


21. SC 


V 


1-.5S 




-0.33 


1".30 


11CS.S5 


-10- 


11.06 


551 


3325 


-0.361 


-""6 


-115 


-0.107 


Macedonia 


is. - : 


o 


15.16 




-0.15 


13. "5 


-S6.5S 


525 


11.13 


445 


SOS 


-0.153 


-20 


-11 


-0.0S4 


Moldo\ _ a 


22.90 




20.20 




-0.12 


17.71 


250.1" 


1266 


13. S5 


174 


633 


-0.2 IS 


-634 


-116 


-0.400 


Mongolia 


1~.96 




13.94 




-; :: 


15.2.0 


105.2.0 


233 


.1.5= 


162 


427 


-0.240 


194 


56 


0.536 


>. .ornenearo 


1S."0 


o 


9.33 


R 


-0.50 


IS. 66 


333.99 


156 


6.6S 


304 


1SS 


-0.642 


-7 


-30 


-0.0SS 


Poland 


12.20 




12.12 




-0.01 


5.5" 


-61.95 


1"60S 


5.4" 


966 


36S-S 


-0.050 


15235 


505 


1.052 


Romania 


1160 


X 


11.55 




-0.15 


10. 6S 


32S.1" 


"616 


S.-" 


--S 


5632 


-0.2.07 


2016 


120 


0.36- 


?.us sian 7 e der ation 


5.60 


X 


11.60 




0.21 


".51 


319.26 


■-;■■ 


".SO 


52^ 


"---; 


0.035 


2"100 


205 


0.6-3 


Serbia 


18.78 


o 


11.09 




-0.41 


15.13 


333.92 


2533 


7.94 


195 


1787 


-0.475 


-746 


-13S 


-0.415 


Sim ■alia 


16 50 


3 


...... 




-0 35 


14.6S 


1013 56 


5355 


9.23 


::;;• 


= :-- 


jO.371 


790 


123 


.... 


Slovenia 


16.10 


V 


12.59 




-0.22 


13.10 


16-5. "1 


32SS 


9. S3 


2221 


^190 


-0.249 


1202 


576 


0.350 


Tajikistan 


25. 33 




17.10 




-0.-2 


23.65 


15-. 55 


S22 


11.85 


60 


413 


-O.-SS 


--OS 


-Si 


-0.610 


Turkev 


10.55 




1-."- 




0.-0 


S."5 


-22.51 


23 "IS 


11.09 


55- 


"3-61 


0.267 


49742 


5"1 


1.350 


Turkmenistan 


2-. 50 




21.03 




-0.1- 


IS. "6 


316. -S 


1161 


15.47 


^50 


2473 


-0.217 


1312 


l"i 


0.5-5 


Ukraine 


24.35 




17.67 




-0.27 


1S.S2 


-36.52 


226"3 


11.48 


314 


1-539 


-0.350 


-S13- 


-123 


-0.2S1 


Uzbekistan 


22. S- 




21.55 




-0.05 


1S."1 


150.55 


3516 


15.65 


250 


6S2S 


-0.163 


2511 


55 


0.305 



238 



Figure 7 Data Validation Comparison. 

Equation 4.1-4 A Analyses and Comparison 



Table 4.5: Research Que 



i\k-.M-IDI|. -kSE ; „ 



CounU-y Eq 4 
Puikind 30703 



Inly 



266 M 
1n23 1 > 



Sim akin 10638 

li-umia 7971 

Croatia 9255 



Latvia 9380 

Poland 7784 

Hiih'jaiy 10016 



Turkey 5211 

fatgne 7423 

Croatia 7136 



Latvia 6562 

A.-vikniaii 1490 

!va,:iklMaii 3529 



Macedonia 5383 

Bosnia and Herzegovina 5421 

Albania 3275 

Turkmenistan 3134 

Georgia 987 

Armenia 641 

Ukraine 2081 

Seifaia 3724 

Uzbekistan 1410 

Mongolia 

Moldova 2336 



Average 

A'CI. ILV 



("irandc Tola! Awij:a I ii::ikin 



>n Comparison: Dothepre-i 



Eg 4.2 
2 l >6'>5 



26068 
17399 



H1366 
7548 
9343 



7417 
7242 
H)0:2 



6802 



Akaikc'.-. IC Score 



-1-.6 2 

O'Vi 

233ii 



-267 

1332 



Lu ■.' 



26351 
17674 



I"'-- 1'. 
is] -If-. 

H K in? 



■■- IN. 
.-p.: 2 

'.'2.SN 



5036 
4962 
3761 



6 i : ! 2 
24.66 



6 16.19 



Eq 4.4 
329<>l 



25622 
! l )675 



95117 
7725 
7607 



10638 
7967 
:nof)| 



.■-..--■'■' 
7812 
7697 



— — , 
2357 

3 165 



-'.b.\ 
4255 
3 1 64 
2677 
1937 
1391 



HDI. 
■IcOS 
Actual 

212— 

-122-/ 
::■■■<.■■!■■■: 
2 I 1 -.' I I, 
2"0q ' 
I'J'M'.i 
I - : !■■.-■'. I 
li.C-O, 
9980 ' 
9I68 1 
9097 

—22^2, 
7972 
7820 
6798 
6744 
6182 
5718 
5255 
4791 
3480 
3458 ' 
3261 
2940, 
29 1 1) 
2487 ' 
2331 
2108 
1931 
1779 
1763 



8564 
8455 



609.95 
81.38 



Shadow Economy", and I:diica[ion L\penJ id 



together explain the change in Income per capita? 



Research Question #4 Equations Comparison 
Against Actual 2008 Income per Capita 




— Eq 4.1 — Eq 4.2 — Eq 4.3 — Eq 4.4 



-Actual 



239 



Table 7.2 Data Validation Equation Analysis. 





Equation 


1 



> 

o 
U 


.2 
< 


o 
tin 


i 

o 

6b 
Q 

o 
- 


.0 
o 


1 
o 
U 

'a 

'in 


i 

o 


I 
2 


a 

1 

1 
& 

% 




1 




o 

'C 

o 
< 


o 

D. 
O 

> 

p 
1 


o 



s 


> 




Data Analysis 




































2.1a 


Ho: Die, ;S HDI IOT0 




95% 


0.7997 


124.81 




Y 


Y 


Y 


0.85555 


2.37/2 


Y 


5.49/1 


.07/1 


81.2482 


7.93/4 






2.1b 


Ho: DIcj^HDI,,,,, and SE, 0I)! 






0.8248 


73.97 




Y 


Y 


Y 


0.78156 


2.36/5 


N 


1 .02/2 


.46/1 


77.8885 


3.84/8 


X 










































3.1 


Ho: DEEc, # SE, |, S 


56 


95% 


0.0012 


1.04 


55 


N 


V 


Y 


0.57088 
























































4.1 


Ho: D3Ic f HDI^go + SH 2lK)S + EEc, m( , + EEc 3 2 oos 


36 


95% 


0.6714 


18.88 


55 


N 


V 


Y 


1766.5 


1948/14 


Y 


5.04/4 


2.41/1 


645.1 


26.93/19 


1.4 




4.2 


Ho: D31c * HDI, 990 + Sh, + %D3EEc 


56 


95% 


0.6698 


24.66 


55 


N 


V 


Y 


1770.8 


14.27/9 


Y 


7.98/3 


.52/1 


644.425 


22.77/13 


1.7 




4.5 


Ho:D3le^ IIDI +SEJ008+ $D3EE 


56 


95% 


0.6502 


22.69 


55 


N 


Y 


Y 


1822.4 


25.39/9 


Y 


10.37/3 


.96/1 


646.495 


36.72/15 


2.8 




4.4 


Ho: D3lc = IIDI,,,,, + SEjoos + $D3EEc 


56 


95% 


0.8753 


81.38 


55 


Y 


Y 


Y 


1097 


8.75/9 


N 


6.53/3 


.06/1 


609.948 


15.13/13 


1.2 




4.5 


Ho: D31c *IID1,„„ + SE,,„ + $D3EEc + Group 


36 


95% 


86.95 


59.3 


55 


N 


Y 


Y 


1113.1 


15.01/14 


N 


5.35/4 


.06/1 


611.855 


18.42/19 


0.9 












































Data Testing lor Validity and Reliability 




































1 


Ho : HDI,„„ i GDPI 19M + LEI,,,,, + EI„, 




95% 


0.9148 


119.18 




Y 


Y 


Y 


0.01863 


















2 


Ho : HDI, 007 f GDPI 2007 + LEI, „ 7 + EI 2007 




95% 


0.9997 


44500 




Y 


Y 


Y 


0.00117 
























































3 


Ho: DHDI = DCDPI+ DLE1+ DEA1 




95% 


0.1882 


8.19 




Y 


Y 


Y 


1.6824 


















4 


Ho : Die m DHDI 




































5 


Ho: Mean Ave ofSE Set = Mean Ave of SE Sample Set 


185 


99.90% 






185 
























-2.4559 


6 


Ho: Mean Ave SH Sample Set M1M1C6 =Mean Ave SE Sa 


25 


99.9 






22 
























-4.8587 








































7 


Ho: DIc=SE 19 9 + SE2 007 


56 


95% 


44.12 


14.82 


55 


N 


Y 


N 


2505 


















8 


Ho: Die - SE,„ 


56 


95% 


32.44 


17.8 


55 


Y 


Y 


Y 


2533 


















9 


Ho: Die = SBjoj 


56 


95% 


45.73 


28.88 


55 


Y 


¥ 


Y' 


2270 
























































10 


1 lo: Me:m Ave LL else! - Mean Ave LL: Sample Set 


171 


95% 






169 
























1.1723 




5.1 


Ho: D3k# SE, 00S + %D3EEc 




95% 


0.9176 


118 




Y 


Y 


Y 


0.5815 


8.60/5 


Y 


5.83/2 


.56/1 


41.5533 


12.99/8 


X 




5.2 


Ho: D3Ie + SE2008 D3EE 




95% 


11.6049 


17.07 




N 


Y 


Y 


1.2737 


3.72/5 


N 


3.55/2 


.78/1 


75.8537 


8.05/8 







240 



Table 7.3 Data Sources. 



A 2010 Human D evelop ment Rep ort D ata Trends & r 1 9 9 thro ugh 2009. (HDR, 20 1 : URL . http : hdr.undp .o rg en statistic s : ) 
B Own Calculations. Average growth rate is a 2 -year rolling average. 

Schneider, Priedrich Georg & Enste, Dominik K (2000). Shadow Economies Around the World: Size, Cause a, and Consequences. International 
C Monetary Pund, Working Paper 00 26, 56. Table 2. 

D (200 9 e). Pluman Development Rep ort New York, XY: United Nations Development Programme. 
E UNE SCO Institute £ r Statistic s (2 1 0). 

Schneider, Priedrich Georg, Buehn, Andreas & Montenegro, Claudio E. (2010). Shadow Economies all over the World: Xew Estimates for 1 62 
P Countries from 1999 to 2007. World Bank Working Paper Series, 5356, 53. Table 6. 
G (200 9 e). Pinman Development Report Xew York, XY: United Xations Develop ment Pro gramme. 
H 201 Human Development Report Data Trends for 1990 through2009. (HDR, 2010, URL. http: hdrstats.mdp.org eatables deMt.html) 

YatrEiat Clifford Zinnes (2000). The Evolution of the Shadow Economy in Transition Co untries: Consequences fir Economic Growth and Donor 
I Assistance. C AER II Discussion Paper Xo. 83. Table 1. 

J The Pormer Socialist Pederal Republic of Yugoslavia states, including Bosnia and Herzegovina, are represented by the data for Macedonia 
K Ahmet Burcin Yereli Ibrahim Erdem Secilmis, AlparslanBasaran, (2007) Shadow Economy an Public Debt Sustauiability in Turkey, DOT 1 0.229 S 

Schneider, Priedrich Georg, Buehn, Andreas & Montenegro, Claudio E. (2010). Shadow Economies all over the World: Xew Estimates for 162 
L Countries from 1999 to 2007. World Bank Working Paper Series, 5356, 53. Table 6. Average for Transcaucasia. 



Schneider, Priedrich Georg, Buehn, Andreas & Montenegro, Claudio E. (2010). Shadow Economies all over the World: New Estimates for 162 
M Countries from 1999 to 2007. World Bank Working Paper Series, 5356, 53. MIMIC 6 Pigure. 
X (1 99 S, Table 31). Human Development Report. Xew York, XY United Xations Develop ment Pro gramme. 
O The P ormer S o cialist P e deral Repub lie o f Yugo slavia state s are rep res ented b y the d ata fb r M ace d onia 
P The Pormer Czechoslovakia states are represented by the data for the Czech Republic 
Q Serbia and Kosovo figures are combined 
R Serbia and Montenegro are reported to gether 

Table X in HDR 2 009 for 2007: Health and Education data from UXES CO Institute for Statistics (UNESCO, 2009),Table 19: 
z http: stats.uis.unesco.org. Accessed S 26 2010. 
H TableHhHDR20O9fbr2OO7:SummaryStatisticsCHDR,2OO9). 
M. Table M in HDR 2 00 9 for 2007: Economy and Inequality- (HDR 2009). 
V Zhou, Pujin(20G7). The Shadow Economy in Mongolia Size, Causes and Consequences. Tinb ergen Institute Amsterdam 42. 



241 



Table 4. 1 Correlation Coefficient Matrix. 

Variable IcChDc HDIChLEI EAI 
IcChDcl.0000 
HDIChO.5005 1.0000 
LEI_EAI0.6793 0.2639 1.000 

Table 4.2 Correlation Coefficient Matrix. 

Variable HDI1990 IcChDc SE2008 
HDI19901.0000 
IcChDcO.6532 1.0000 
SE2008-0.5148 -0.5981 1.0000 

Table 4.3 Correlation Coefficient Matrix. 

VariableSE1990 SE2008 EEDc2008 

SE19901.0000 

SE20080. 83591. 0000 

EEDc20 08-0 .4450-0. 5807 1. 0000 

Table 4.4 Correlation Coefficient Matrix. 

VariablelCo Ic u Ic T HDI1990 EEA$c 

lcol.0000 

ICuO.8882 1.0000 

Ic T 0.9971 0.90701.0000 

HDI 1990 0.7260 0.7994 0.73591.0000 

EEA$c0.7763 0.7129 0.7875 0.5704 1.0000 

Table 7.4 Correlation Coefficient Matrix. 

Test: Correlate the change in Total Income per Capita, change in Total Education Expenditures, 
pre -test Human Development Index, pre -test Shadow Economy, change in Life Expectancy In- 
dex, change in Education Attainment Index, post-test Shadow Economy, change in Shadow 
Economy, Country Group (obs=36) 

Variable$Alc3$AEEc3 HDI 1990 SE 20 o 8 ALElAEAI SE 1990 ASEGroup 

$Alc31.0000 

$AEEc3 0.8619 1.0000 

HDI 1990 0.7710 0.5549 1.0000 

SE 2008 -0.687 -0.5285 -0.6040 1.0000 

ALEI 0.0807 0.1046 -0.1254 -0.1996 1.0000 

AEAI 0.2757 0.2394 0.4848 -0.2350 -0.0567 1.0000 

SE 1990 -0.586 -0.510 -0.4409 0.8660 -0.0844 -0.2308 1.0000 

ASE -0.126 0.0618 -0.3071 0.1641 -0.1256 0.0313 -0.3163 

GroupO.6549 0.5661 0.5648 -0.6958 0.3222 -0.0434 -0.6077 -0.089 



242 



GLOSSARY 



Organizations and Acronyms 

Organizations: Affiliated Programs, Reports and Data 
Intergovernmental Organizations (IGO) 
International Governmental Organizations (IGO) 
Non-governmental Organizations (NGO) 
Nonprofit or Not for profit Organizations (NO) 



IGOs 



United Nations (UN): 

United Nations Education, Science, and Cultural Organization (UNESCO) 

Statistical Information System on Expenditure in Education (SISEE) 

United Nations Development Program (UNDP) 

Human Development Report (HDR) 

Human Development Index (HDI) 

Gross Domestic Product Index (GDPI) 

Life Expectancy Index (LEI) 

Educational Attainment Index (EAI) 

Income per capita (Ic) 

Millennium Development Goals (MDGs) 

Millennial Development Goals Report (MDGR) 

United Nations Infants and Children's Emergency Fund (UNICEF) 

World Bank (Bank): 

World Bank Group (WBG) 

Global Monitoring Report (GMR) 

International Comparisons Program (ICP) 

World Bank Development Economics Research Group (DERG) 

World Development Report (WDR) 

World Development Indicators (WDI) 

World Governance and Anticorruption Indicators (WGI) 

Business Environment and Enterprise Performance Survey (BEEPS) 

World Bank Human Development Network (HDN) 

Education Statistics (EdStats) 

The International Monetary Fund (IMF) 

Global Monitoring Report (GMR) 

World Economic Outlook (WEO) 

International Financial Statistics (IFS) 

International Accounting Standards (IAS) 

International Financial Reporting Standards (IFRS) 

Organisation for Economic Co-operation and Development (OECD) 



243 



NGOs: International and National Institutes, Policy Centers, and Foundations 

Council on International and Public Affairs (CIPA) 

World Trade Organization (WTO) 

Brooking Institution (BI) 

Transparency International (TI) 

Global Corruption Report (GCR) 

Corruptions Perceptions Index (CPI) 

Global Corruption Barometer (GCB) 

Bribe Payers Index (BPI) 

National Integrity System (NIS) 

The Heritage Foundation (HF) 

Index of Economic Freedom (EFI) 

Governmental Bodies and Data 

United States (US) 

CIA World Fact Book (CIA) 

US Library of Congress (LOC) 

United States State Department (DOS) 

Countries and Regions: Background Notes 

United States Agency for International Development (USAID) 

System of National Accounts or National Income accounting (NI) 

European Commission (EU) 

Europa World Fact Book (Europa) 

European Statistics (Eurostat) 

European Statistics, Data, and Metadata Exchange (SDMD) 

European Bank for Reconstruction and Development (EBRD) 



244 



Organizations and Acronyms - Glossary (Continued) 



Glossary: Data Legend 






Variable 


Population 


Per Capita 








1990 


2007 


Change 






1990 


2007 


Change 


Human Development Index 




HDI 


HDI 1990 


HDI 20 o7 


AHDI 












Actual Years Measured 




1990 


2007 
































Shadow Economy SE 




SE 


SE1990 


SE 2 oo7 


ASE 












Natural Log of SE 


In SE 


lnSE im 


In SE 2007 












Actual Years Measured 




1989-1999 


2000-2007 


































Gross Domestic Product 




GDP 


GDP 19 9o 


GDP 2007 


AGDP 




Ic 


IC 1990 


IC 2007 


Ale 


Official - on the books 


GDP, 


GDPi [990 


GDP, 2007 


AGDP, 


Ic, 


I c l 1990 


I c l 2007 


Ale, 


Shadow Economy - on the ground 


GDP 2 


GDP 21 99c 


GDP 22007 


AGDP 2 


Ic 2 


I c 2 1990 


ic 2 2007 


AIc 2 


Total 


GDP 3 


GDP3 1990 


GDP 32007 


AGDP 3 


Ic 3 


I c 3 1990 


IC3 2007 


AIc 3 


Actual Years Measured 




1990 


2007 






1990 


2007 


























Education Expenditure 




EE 


EE 1990 


EE 20 o 7 


AEE 




EEc 


EEC 1990 


Eec 20 o7 


AEEc 


Official - on the books 


EE, 


EEi 1990 


EEi 20 o 7 


AEE, 


EEci 


EEC] ]99o 


EEc, 200 7 


AEEci 


Shadow Economy effect - on the ground 


EE 2 


EE 2 1990 


EE 2 20 o 7 


AEE 2 


EEc 2 


EEC 2 1990 


EEc 2 20 o7 


AEEc 2 


Total 


EE 3 


EE3 1990 


EE 3 2007 


AEE 3 


EEc 3 


EEC3 ]99o 


EEC3 2 oo7 


AEEc 3 


Actual Years Measured 




1989-1995 


2000-2007 






1989-1995 


2000-2007 
























Government Expenditure 




GE 


GE 1990 


GE 2007 


AGE 












Official - on the books 


GE, 


GE] 1990 


GEi 20 o 7 


AGE, 










Shadow Economy effect - on the ground 


GE 2 


GE 2 1990 


GE 2 20 o 7 


AGE 2 










Total 


GE 3 


GE3 1990 


GE3 2 oo 7 


AGE 3 










Actual Years Measured 




1990 


2007 













Several agencies are sub-divisions of the United Nations (UN): the United Nations Edu- 
cation, Science, and Cultural Organization (UNESCO), the United Nations Development 
Program (UNDP), United Nations Infants and Children's Emergency Fund (UN1CEF), the Hu- 
man Development Program (HDP), and the Millennium Development Goals (MDGs). Other 
agencies exist within the broader UN system. This, according to the IMF (IMF, 2010g, p. 1). 

The IMF and the World Bank are institutions in the United Nations system. They share 
the same goal of raising living standards in their member countries. Their approaches to this goal 
are complementary, with the IMF focusing on macroeconomic issues and the World Bank con- 
centrating on long-term economic development and poverty reduction. 

Several agencies are sub-divisions of the World Bank (Bank), specifically, the World 
Bank Group (WBG), along with the World Bank Human Development Network and its affiliates, 
the World Bank Development Economics Research Group, and the World Bank Education and 
Development Research Groups, which produce the World Governance and Anticorruption Indica- 
tors (WGI), and Education Statistics (EdStats). The International Monetary Fund (IMF) co- 
produces the Global Monitoring Report (GMR), sharing its banking and financial statistics for the 
Millennial Development Goals (MDGs) and the network of agencies. Regional and national 
agencies such as Organisation for Economic Co-operation and Development (OECD), Europa 
World Fact Book (Europa), European Commission (Eurostat), CIA World Fact Book (CIA), The 
US Library of Congress (LOC), and the United States State Department (DOS) all research, com- 
pile, share, and report research, information, and data. An agency or an affiliated institute, think- 
tank, or foundation may employ researchers adding to this network of information, research, and 
data. Senior Fellow at the Brookings Institution, Daniel Kaufmann, previously "served as a di- 
rector at the World Bank Institute, where he pioneered new approaches to measure and analyze 
governance and corruption, helping countries formulate action programs" (201 Of, p. K). 



245 




Policy Problem 

2007 



On the 
Books 



Fiscal 1^^^^ Development 
Policy 1 1 Policy 

I 



f> Infrastructure 



* Health 



> Other 



On the 

Ground 

Shadow 1 I Corruption 

GDP 2 I Remuneration 



~~H E 



Education 



1988 



State-building Process 
1992 



1 



Transition Process 
1996 



2000 



Figure 8 The Policy Problem. 



246 



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